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3 Statistical Analysis Methods You Can Use to Make Business Decisions

Business professionals using statistical analysis methods

  • 15 Dec 2021

Data is a driving force in business. More information is being collected than ever before, which professionals continually seek to leverage for success. Across all business functions, it’s essential to have analytical skills to interpret data and put it to use.

Statistical analysis is the basis for many business analytics approaches. Gaining a firm understanding of different statistical analysis methods is one of the first steps to unlocking the power of business analytics. With this knowledge, you can make sense of data, project future outcomes, and make more informed decisions.

Related: Examples of Business Analytics in Action

Below are three helpful statistical analysis methods that lead to better business decisions.

Access your free e-book today.

Statistical Analysis Methods for Business

1. hypothesis testing.

Hypothesis testing is a statistical method used to substantiate a claim about a population. This is done by formulating and testing two hypotheses: the null hypothesis and the alternative hypothesis.

Related: A Beginner’s Guide to Hypothesis Testing in Business

The null hypothesis (denoted by H₀) is a statement about the issue at hand, generally based on historical data and conventional wisdom. A hypothesis test always starts by assuming the null hypothesis is true and then testing to see if it can be nullified.

The alternative hypothesis (denoted by H₁) represents the theory or assumption being tested and is the opposite of the null hypothesis. If the data effectively nullifies the null hypothesis, then the alternative hypothesis can be substantiated.

In business, hypothesis testing is an effective means of assessing theories and assumptions before acting on them. For managers, leaders, and those looking to become more data-driven, this method of statistical analysis is a helpful decision-making tool. Putting this practice into action can lead to better foresight and positive outcomes when planning a business’s future.

For example, you might conduct a hypothesis test to substantiate that if your company launches a new product line, sales and revenue will increase as a result. Since this initiative would be expensive, your company might launch the product in a small test market and use the data it collects to justify rolling it out on a larger scale.

Hypothesis testing is a complex yet highly valuable statistical method for business. If you want to learn about hypothesis testing in more detail, taking an online statistics or business analytics course can be worthwhile.

2. Single Variable Linear Regression

Linear regression analysis is used for two main purposes: to identify and evaluate the relationship between two variables and forecast a variable based on its relationship to another one.

In single variable linear regression analysis, the relationship between a dependent variable and an independent variable is evaluated by identifying the line of best fit.

To find the line of best fit, use the following equation:

Single Variable Linear Regression Formula

Here, ŷ represents the expected value of the dependent variable for a given value of X, which represents the independent variable. α is equal to the Y-intercept, or the point at which the regression line crosses the Y-axis, when X is equal to zero. β is the slope that equals the average change of the dependent variable (Y) as the independent variable (X) increases by one. Finally, ε is the error term that equals Y – ŷ, or the difference between the actual value of the dependent variable and its expected value.

Using this method, you can forecast a defined variable based on known data.

Consider the relationship between advertising spend and revenue, for example. A business can use historical data relating the advertising dollars spent to the amount of revenue generated for various campaigns or time periods. Using a single variable linear regression analysis, it can use that information to find the line of best fit and subsequently use the slope to forecast revenue for future campaigns.

Business Analytics | Become a data-driven leader | Learn More

3. Multiple Regression

Whereas single variable linear regression analysis studies the relationship between two variables—a dependent variable and an independent variable— multiple regression analysis investigates the relationship between a dependent variable and multiple independent variables.

Forecasting with multiple regression analysis is similar to using single variable linear regression. However, instead of entering only one value for an independent variable, a value is input for each independent variable. Using the same notation as the single variable linear regression equation, the following equation applies to multiple regression:

Multiple Regression Formula

In business, multiple regression analysis is helpful for predicting the outcomes of complicated scenarios. For example, think back to the relationship between advertising spend and revenue. Instead of looking at total advertising expenditures, you can use multiple regression analysis to evaluate how different types of campaigns, such as television, radio, and social media ads, impact revenue.

Credential of Readiness | Master the fundamentals of business | Learn More

Developing Your Analytical Skills

An analytical mindset is essential to business success. After all, data is one of the most valuable resources in today’s world, and knowing how to leverage it can lead to better decision-making and outcomes.

Related: How to Improve Your Analytical Skills

Depending on your current knowledge of statistics and business analytics and long-term goals, there are many options you can pursue to develop your skills. Taking an online course dedicated to honing and applying analytical skills in a professional setting is a great way to get started.

Do you want to leverage the power of data within your organization? Explore our eight-week online course Business Analytics —one of three courses comprising our Credential of Readiness (CORe) program —to learn how to use data analysis to solve business problems.

business statistics problem solving

About the Author

Library Home

Introductory Business Statistics

(9 reviews)

business statistics problem solving

Lex Holmes, University of Oklahoma

Barbara Illowsky, De Anza College

Susan Dean, De Anza College

Copyright Year: 2017

ISBN 13: 9781947172470

Publisher: OpenStax

Language: English

Formats Available

Conditions of use.

Attribution

Learn more about reviews.

Reviewed by Yan Wang, Instructor, Rogue Community College on 11/29/23

I would like to propose the addition of a section on non-parametric statistics to the textbook, because this section is required in our study, I will have to find additional teaching resources. read more

Comprehensiveness rating: 4 see less

I would like to propose the addition of a section on non-parametric statistics to the textbook, because this section is required in our study, I will have to find additional teaching resources.

Content Accuracy rating: 5

I did not see any mistakes.

Relevance/Longevity rating: 5

The book is up-to-date.

Clarity rating: 3

This is about the Solutions to Practice. I use chapter 8, section 8.2 as an example. I'd like the problems to be numbered sequentially, and the questions within each problem to be lettered alphabetically. This way, the solution menu can reference the problem number rather than specific question numbers. Because the solution menu provides only answers to the even number questions, so for the problem with multiple questions does not have complete answer.

Here's an example of how I suggest structure the information: Problem 1: Question a: [Text of question a] Question b: [Text of question b] Question c: [Text of question c] Question d: [Text of question d] Question e: [Text of question e] Then, in the solution menu, you can refer to "Problem 1," "Problem 2," and so forth, providing solutions for the entire problem rather than specific questions, this way, users can easily find the solution to the entire problem without seeing only partial answers to the questions.

Consistency rating: 3

The solution menu of chapter 8 provides answers to even number questions, but solution menu to chapter 9 provides answers to odd number questions. I expect the pattern will be either to all even number of questions or to all odd number of questions for all the chapters, or provide answers to all the questions on the instructor's side.

Modularity rating: 5

I like the layout.

Organization/Structure/Flow rating: 5

Well organized.

Interface rating: 5

I did not find any navigation problems.

Grammatical Errors rating: 5

Did not see any grammatical errors.

Cultural Relevance rating: 5

Did not find anything wrong with this topic.

It's a great book, well written, beautiful layout. MANY THANKS!

Reviewed by Jaehwan Jeong, Associate Professor, Radford University on 10/24/23

This textbook covers most chapters that a typical introductory statistics book includes. The chapters are well organized with essential topics. This will be enough to teach an introductory statistic for a semester. It has many examples for every... read more

Comprehensiveness rating: 5 see less

This textbook covers most chapters that a typical introductory statistics book includes. The chapters are well organized with essential topics. This will be enough to teach an introductory statistic for a semester. It has many examples for every topic in the right place. I especially like the key terms, chapter review, and formula review at the end of each chapter. They summarize the essential topics of the chapter very well. The page format is simple and clean to help students focus on the text. Overall, I think there will be no problem to use this book for an introductory statistics course.

Content is accurate, error-free, and unbiased.

This book is very well organized with essential knowledge for an introductory statistics course.

Clarity rating: 5

This book is easy to read and understand. I like the writing style of this book.

Consistency rating: 5

This book keeps a high consistency with a neat format for every chapter and section. It also places examples after an introduction of a concept and other things by following the same format. It will help the instructor and students make the course as a weekly routine.

This book is flexible for the needs of a course as it divides topics by sub-chapters.

This book organizes chapters in the right order to learn more advanced concepts in the following chapters. I find no problem in the organization of materials in the book.

This book uses a very good and simple format. It helped me to quickly find the key topics of each sub-chapter. It shows graphs in a very clear and nice way.

I found no typos or grammar errors.

I do not find any cultural issues in this book.

This book is very well written by combining essential topics for an introductory statistics course. It does not include a term project, but an instructor can find one from other sources. I wish that it also provides an online quiz bank.

Reviewed by Shengnan Fang, Full-time Faculty, Linn-Benton Community College on 1/7/21

This textbook covers the major topics in the introductory of statistics. There are 13 chapters, and the first 3 chapters focus on the introduction of data, descriptive statistics and probabilities. From Chapter 4 to Chapter 7, those chapters... read more

This textbook covers the major topics in the introductory of statistics. There are 13 chapters, and the first 3 chapters focus on the introduction of data, descriptive statistics and probabilities. From Chapter 4 to Chapter 7, those chapters introduce the basic concepts in both discrete random variables and continuous random variables. This textbook also covers the confidence intervals, hypotheses tests, ANOVA and simple linear regression. In my opinions, those chapters are explained and organized consistently and easy to follow. However, as an introductory statistics textbook for students majoring in Business, I think this textbook probably doesn’t provide more relevant examples in Business. For example, in CH4, there are only a few examples related to business. As for a one quarter business statistics class, 13 chapters are still a little bit lengthy. For example, the textbook introduces normal distribution in Chapter 6, and Central Limit Theorem in Chapter 7, it might be appropriate to put Central Limit Theorem as a section in Chapter 6.

The contents of this textbook are accurate, error-free and unbiased.

Relevance/Longevity rating: 4

When I began to read this textbook, I expected to see some application of Excel or even R, as using these software or programming language would be greatly helpful for our undergraduate students. However, I didn’t see those applications in this textbook. Although including the application of Excel might make the textbook lengthy, I think it might be useful for instructors to use show those applications to students and strengthen their understanding about how to use Excel or R to do basic business analysis.

Clarity rating: 4

This textbook is well organized consistently and easy to follow. It also provides numerous graphs and figures to visualize the statistical analysis.

The contents of this textbook are internally consistent in terms of terminology and framework. Each chapter begins with an interesting statistical topic in reality, and then follows the “Terminology” section to introduce key concepts. It also provides examples for each section to strengthen students’ learning about new contents. At the end of each chapter, it summarizes the key terms, chapter review, formula review, which would be convenient for students to grasp the major contents.

There are 13 chapters in this textbook, and after reading each chapter, I think the text can be readily divided into smaller reading sections. For example, if I just want to introduce Chi-square distribution, then I can assign Chapter 11 for students to learn without asking them to read Chapter 7, which focuses on Central Limit Theorem.

Organization/Structure/Flow rating: 4

Overall, the contents are well organized in a logical fashion. However, I want to point it out the Venn Diagrams in Chapter 3 taking much more spaces than it supposes to be. For example, from page 164 to page 167, the Venn Diagrams take nearly a half page, which are not well organized in the flow. On page 292, the format of key terms are not well aligned and seem to be a little bit messy to follow.

Download the textbook online is easy and the hyperlinks in each section work well.

I cannot recall any grammatical errors in the textbook.

I think there is no culturally offensive content.

As an instructor to community college students, I think the content of this textbook is easy to go through for an introduction class of Business Statistics. However, I think a textbook designated for teaching statistics for students majoring in Business, this textbook doesn't provide enough business analysis examples. And I would also recommend the authors can add some applications of Excel or R to make statistical analysis more applicable for both students and instructors.

Reviewed by Nasim Sabah, Assistant Professor, Framingham State University on 6/2/20

This textbook covers all the relevant chapters for a one-semester Business Statistics undergraduate class. Some of the concepts could more details (e.g., hypergeometric distribution, uniform distribution, separating simple and multiple linear... read more

This textbook covers all the relevant chapters for a one-semester Business Statistics undergraduate class. Some of the concepts could more details (e.g., hypergeometric distribution, uniform distribution, separating simple and multiple linear regression) while some other concepts could be added relevant to business students (e.g., expected returns, variance, standard deviation, log-normal distribution, two-factor ANOVA). Some chapters do not include enough examples (e.g., Chapter 4) and some other chapters do not include examples relevant to business students (e.g., Chapter 3).

The contents seem to be accurate, unbiased, and without any gross errors.

Relevance/Longevity rating: 3

The statistical concepts are not going to change anytime soon, so the materials would be relevant probably for a long time. However, the presentation of examples and most importantly, the lack of business examples and the lack of data in Excel (or other formats) are going to be a bog issue for future instructors and students. As an Instructor, I love to demonstrate examples using MS Excel in class, and the lack of Excel data is a big concern for me. And, there is no guidance for using data analysis software (MS Excel, R, and others). It would be difficult to compete with the publisher textbooks who provide these supports.

Overall, the text is clear, easy to understand, and concise. Some chapters have lots of graphs and examples. However, some concepts are very short and without many examples which makes it harder to grasp the concept. Also, elaborating some concepts would provide a better understanding to some concepts, such as, separating sections for simple and multiple linear regression model.

The book is consistent in terms of concepts, materials, annotations, and chapter structure.

The chapters are independent of each other, and a chapter can easily be added or skipped based on individual needs.

The topics are well organized, and the flow is smooth. As a minor suggestion, I would love to see reorganization of few concepts, such as hypergeometric distribution after geometric distribution in Chapter 4, and a short explanation of normal distribution in Chapter 5 and why it deserves to be a separate chapter (Chapter 6). Also, separating sections for simple and multiple linear regression model in Chapter 13 would make the structure more interesting.

I did not find any interface issue. Both online and PDF versions work well without any distortions.

I did not find any grammatical errors.

I did not find anything insensitive or offensive. The texts and problems seem inclusive and unbiased.

Overall, this is a book with the minimum number of chapters needed for an introductory business statistics course. Some chapters and concepts could have been more elaborate with business relevant examples. One concern is the data availability for students to work on different concepts. Providing the data in Excel format would make the textbook much more attractive. A reliable and automated homework/quiz platform would be nice too, but given that this is a free textbook, it is worth a try.

Reviewed by Marta Maras, Assistant Professor, Gettysburg College on 4/22/20

All relevant chapters covered in most undergraduate introductory statistics classes are included and explained in a consistent and clear way that keep students engaged. Considering that the book is intended to be used by students majoring in... read more

All relevant chapters covered in most undergraduate introductory statistics classes are included and explained in a consistent and clear way that keep students engaged. Considering that the book is intended to be used by students majoring in business, the application of statistical methods and tools in the business setting could have been more pronounced. There are a few chapters (for example, on probability) that barely mention any type of statistical problem set in the realm of management, finance, marketing, HR, etc. The book provides an effective index at the end, but not the glossary. Though not an issue, the students should be instructed to find the term in the index and search for the definition in the corresponding chapter (each chapter ends with key terms and a review which is quite helpful).

The content of the book seems free of any gross errors and biases.

The standard statistical concepts that the book covers will not change any time soon. Though the lack of business-specific (or pop culture) examples might be a missed opportunity, providing the typical cards, balls and student GPA examples makes the book less likely to be dated in the next decade.

The text is clear, easy to follow and understand. The authors provide numerous examples to make the concepts comprehensible. They also use visual tools, such as tables and figures, well to keep the students’ attention and enhance the understanding of the statistical problems at hand.

The book is consistent in terms of language, tone, annotation and chapter structure (introduce, give basic examples, build, add more complex problems, finish with reviews and practice problems).

The flow of the chapters is logical and can be easily divided into smaller sections. There are a number of subsections in a chapter that can be included or skipped based on the individual course learning goals. A number of chapters (especially sub-chapters) overlap with the authors’ general statistics book that is also part of the Open Stax library and includes additional chapters (can be combined in an extended course syllabus).

Overall, the topics are organized well in a logical fashion. There were a few instances in the book where individual instructors would choose to cover a specific sub-chapter earlier or later in the course, mostly to follow the research process (from a question, hypotheses, design, data collection, analyses, interpretation…). However, since chapters are easily divisible, a different flow of topics can be easily arranged based on course needs and learning goals.

Interface rating: 4

The authors have provided a number of problems and concepts with visual representation. Using visual tools in introductory courses is very welcome and enhances student understanding. Though some images and charts vary in size and detail (x-y axes), I haven’t found any that are distorted to the point of confusion. Use of colors and notation works reasonably well on different platforms.

The text and questions are clearly and correctly worded.

The text and problems in the chapters seem inclusive, not culturally offensive or insensitive. A number of examples mention different races, ethnicity, political affiliations, but in a neutral tone, without bias.

Students will appreciate each chapter ending with key terms, a chapter review, a formula review and a long set of practice problems. As instructors, we frequently have students ask for additional problems to work on in order to prepare for tests and/or to understand the concept variations better. This book provides plenty of problems for them and wraps up each chapter with more homework problems. Solutions are provided at the end.

Reviewed by Mark Segall, Professor, Metropolitan State University of Denver on 7/10/19

The textbook covers all of the main topics for a typical one semester Business Statistics course: descriptive statistics, probability, discreet and continuous distributions, central limit theory and confidence intervals, hypothesis testing for 1,... read more

The textbook covers all of the main topics for a typical one semester Business Statistics course: descriptive statistics, probability, discreet and continuous distributions, central limit theory and confidence intervals, hypothesis testing for 1, 2, or many samples, Chi-Squared distributions, and simple and multiple linear regression.

Some of the chapters could use more details if the reader wants a more comprehensive coverage of the topics. It would be up to the instructor using this textbook to supplement textbook with details that they deem important. One example is in the descriptive statistics chapter where there could be an explicit discussion of the difference between frequency distributions and graphs for nominal versus ratio data. Another example is in the ANOVA chapter which does not cover Two Way ANOVAs or block designs.

The accuracy is very good in this textbook. However, one area of concern, which is often hotly debated, is found in the Linear Regression and Correlation chapter where there is a discussion of how independent variables will have a significant effect on the dependent variable. Saying that one variable has a significant effect on another variable should only be done in the context of an experimental design. Correlational analysis can only suggest a cause and effect relationship or allow us to make predictions.

The fundamental topics in this textbook are very stable. There should be little difficulty with the longevity of the textbook.

The text does a good job of concisely describing the topics. There a many unique descriptions of concepts that made the book enjoyable to read.

There were no problems with consistency.

The chapters do well standing independently of each other. They are also well organized internally with practice problems and homework problems at the end of each chapter.

The textbook does not deviate from the organization found in most business statistic text books. One minor difference from the typical structure is the combination of frequency distributions and graphs with the topics of central location and variable in a single chapter.

I read some of the textbook using the Kindle but on a cell phone. While this made the book easily accessible, I would recommend using a tablet or browser.

There were very few errors noticed while reading the textbook. There were recent changes made in the text book at the time this review was written based on the History section found on the browser version. One correction that could be made is in section 9.1: “that is set my the analyst” should be “that is set by the analyst”.

There were no noticeable problems in the examples or homework problems.

In most business statistic textbooks, there is usually the problem of there being too many chapters and sections to cover in a single semester and it is the job of the instructor to select which topics to cover and which to ignore. As I was reviewing this book I was struck more by what was missing than want was in the book. But that might be as much my own expectations of what should be in a business statistic textbook. This textbook covers the minimum number of topics and depending on what is taught in a particular course might require supplemental coverage. For example Time-Series Forecasting is not included, but this might not be a problem in many courses. I was also concerned that there is no mention of the terms ‘false positive’ and ‘false negative’ when introducing Type I and Type II errors, but this is something I can cover in class lectures.

business statistics problem solving

Reviewed by Dawn LoweWincentsen, Director, Portland Metro Campus Library/ Associate Professor, Open Oregon Educational Resources on 4/8/19

In reading and reviewing this resource it is very complete, but very specific to business statistics. All statistical components and aspects are taken into consideration in a factual way, but not always tied back to the wider research process. The... read more

In reading and reviewing this resource it is very complete, but very specific to business statistics. All statistical components and aspects are taken into consideration in a factual way, but not always tied back to the wider research process. The examples and ability to try out the concepts throughout the chapter keep the readers mind engaged and active.

This resource is what I would expect from a business statistics book. It covers the material in a factual, clear manner.

There are no cultural or popular references in this resource that will date it in a few years. It uses tried and true example such as playing cards or sports to demonstrate the topics. It does occasionally refer to "this course" but does not specify the course, and is easily adaptable to any relevant course.

The resource is clearly written with relevant examples and context. It is easy to understand and follow within each chapter and from chapter to chapter.

Consistency rating: 4

The text is consistent in language and structure.

The chapters of this resource stand alone well, and can be easily remixed or used individually to cover a specific concept. Much of the book also overlaps with the Open Stax more general statistics book.

Organization/Structure/Flow rating: 3

The organization seems to be more along the statistical needs of the students, than the research process itself. However, with the ease of modularity each chapter can stand alone, and easily be remixed to the instructor's liking.

Using both an online copy and a PDF copy this text works well with no distortions or interface issues. In the PDF edition it can be onerous to scroll through the end of chapter problems if one is not completing them.

Grammatical Errors rating: 4

The resource meets with American English grammatical standards and expectations. It is easily read at an introductory or early college level.

Cultural Relevance rating: 4

This resource is culturally neutral. There is minimal mention of qualitative data, and no bias or cultural references given in any of the examples or data described.

The number of student problems in each chapter is astounding. Students who complete (sometimes more than 100) problems on a given topic will understand it quite well.

Reviewed by William M. Easley, Instructor (Business Statistics), University of New Orleans on 5/21/18

How one assesses the comprehensiveness of this text depends on one’s purpose. It is purportedly designed for a one-semester course. For that (at least relative to business students at UNO), it is too long -- and too long on the mathematics. For a... read more

How one assesses the comprehensiveness of this text depends on one’s purpose. It is purportedly designed for a one-semester course. For that (at least relative to business students at UNO), it is too long -- and too long on the mathematics. For a two-semester course, at least for our purposes, it is too short. For example, there is no discussion of 2-factor Anova, RBD, etc. However, there is much to admire about the way that the authors present the ideas.

I spent about four hours reading various parts of the text and found no sign of bias or any gross errors. One can quibble over some of the definitions, e.g., that a discrete random variable must have only integral values. I saw a typo or two -- e.g., an SStotal that should have been an SSbetween. As with any book, there are probably others. But let me emphasize that I am not a professional statistician.

Introductory statistics is a little bit like Latin, a ‘dead language’. The basics aren’t going to change. However, the “statistics education community” -- if there is such a thing -- seems to be in a tizzy these days over how to incorporate ‘big data’, etc. into such introductory courses. Some now use the term ‘data science.’ This text is definitely an old-fashioned and rather ‘mathy’ approach (not a bad thing in my eyes). Surprisingly, calculus techniques make an appearance toward the end of the book (the average business student will have little or no idea of that). But, aside from some instructions for using Excel for regression analysis (why not do this for Anova as well?), there is little guidance for technology. In the chapter on the F-distribution, where did those p-values come from? TI-83? Excel? StatTrek? Or did I miss something? Most of the current business stats texts give directions for using Excel, TI-83/4, Minitab, R or all of these. How is this text going to compete with those? I reckon that individual instructor/department could make amendments, but how many would be willing to?

Overall, I like the breezy writing style. But it is a bit bipolar, occasionally almost patronizing and then rather technical. Some terms are used which the student audience has virtually no chance of understanding. Better to omit those or provide explanation. On the other hand, since few students these days actually read books, particularly math books, anyway, why not let the authors express themselves in a way that they find logical and intellectually appealing? My students depend on me to explain the material, or, if I fail them, YouTube.

The text seemed terminologically consistent to me. I do recall a spot in the Anova section where the use of n (nT?) and nj may cause confusion.

Modularity rating: 3

Introductory statistics is not a very modular sort of subject -- it is more a continuous development. For example, the concept of p-value is introduced in chapter 9 of virtually every stats text. But the ‘p’ in p-value is for ‘probability’ and so the student needs to understand the material that chapter, 3 or 4 in virtually every text. Otherwise, I liked the presentation given here in ch. 4 on discrete distributions, but since the authors very nicely tie them together, that material is not presented in a ‘modular’ fashion at all.

The topics of the text are presented in the normal progression. There is some possibility of changing the order of presentation after hypothesis testing (Ch.9), but not before that. Once again, this is how introductory stats works.

Interface is fine, although I absolutely hate scrolling through a pdf. Students would definitely want a print version.

I ain’t found no grammar errors.

Found nothing culturally insensitive. Seems inclusive. All groups are subject to statistics.

Finally, the unscripted part of the review. There is a lot to like about this presentation of the subject. Some parts are quite enjoyable. Here are my criticisms, in order of my view of their increasing importance. 1. The type-setting (if that is the right term) of the formulas in generally pretty lousy. For example, x-bar is always shown with the bar about a mile above the x. The integral on p. 284 looks really bad. Those formulas for r are terrible-looking. I always use MathType for this stuff. On the subject of formulas, why the predilection for ‘computing’ formulas, rather than ‘definition’ formulas? Many authors do this. Who, except programmers, cares how the computer does it? The definition formula offers insight. 2. I like the chapters on discrete and continuous distributions. I think that those on confidence intervals and Anova are not well-written and will be confusing to students. With the chapter on regression, the authors are forced to yield to the complexity of the subject and necessarily trail off into territory that the typical business student has no chance to follow. 3. Ok, here is the 500-lb statistician in the room: My guess is that, except at some rather rarified and/or old-fashioned institutions, virtually everyone teaching introductory statistics now insists on a reliable automated homework/quiz course platform, like MyStatLab. The difficultly/impossibility of doing that is why I don’t write and provide my own free text. We use a Pearson e-book that provides that service to our two-semester sequence for $104.95.

Reviewed by Alan Weber, Full-Time Lecturer, University of Missouri at Kansas City on 5/21/18

Very good for an introductory book. Actually better than the text I've used in the past, covering several key areas such as types of distributions. The authors chose specific enough statistics that students do not need more than the free... read more

Very good for an introductory book.

Actually better than the text I've used in the past, covering several key areas such as types of distributions.

The authors chose specific enough statistics that students do not need more than the free statistics add-on in Excel to use pretty much everything explored in the text.

The text is appropriate in a 1st of 2 statistics courses. It does not cover non-linear regression as would be used to assess likelihood of outcome, it does not cover descriptive clustering, and it does not cover predictive segmentation. It also does not cover time-series analysis.

As a result, it does not cover the techniques commonly employed in business. But it does provide the background necessary prior to learning and use of more advanced topics.

The content appears to be accurate, error-free and unbiased.

This book does not need to change for at least several hundred years. May be good forever, literally.

Really clear, easy to understand. Nice diagrams and examples, many questions and exercises built in. Built to use Excel. World-class for a stats book.

Very consistent and stays within its limits. Doesn't stray from introductory statistics using the Excel stats package.

Very well divided and logically clear.

Flows in the order I would choose. Not need or benefit to cover in anything other than chapter order.

Better than I expected for a PDF. Links work well, sections are logical.

Very clear for a stats book, Questions seemed carefully worded to avoid misinterpretation. Of course, students are very clever when it comes to finding ways to misinterpret, so we'll see once I use it in class.

Unless someone is professionally offended, and looking for ways to claim to be offended in order to further their career or notoriety, it is unlikely in my opinion they will find a fair, reasonable, and legitimate cause to be offended as a result of this text.

Way, way better than I honestly expected.

Table of Contents

  • 1 Sampling and Data
  • 2 Descriptive Statistics
  • 3 Probability Topics
  • 4 Discrete Random Variables
  • 5 Continuous Random Variables
  • 6 The Normal Distribution
  • 7 The Central Limit Theorem
  • 8 Confidence Intervals
  • 9 Hypothesis Testing with One Sample
  • 10 Hypothesis Testing with Two Samples
  • 11 The Chi-Square Distribution
  • 12 F Distribution and One-Way ANOVA
  • 13 Linear Regression and Correlation

Statistical TablesMathematical Phrases, Symbols, and Formulas

Ancillary Material

About the book.

Introductory Business Statistics is designed to meet the scope and sequence requirements of the one-semester statistics course for business, economics, and related majors. Core statistical concepts and skills have been augmented with practical business examples, scenarios, and exercises. The result is a meaningful understanding of the discipline, which will serve students in their business careers and real-world experiences.

About the Contributors

Lex Holmes is a Professor in the Economics department at University of Oklahoma, Norman, OK

Barbara Illowsky is a Professor of Mathematics & Statistics at De Anza College.

Susan Dean is a Professor in the Mathematics department at De Anza College, Cupertino, CA.

Statistics Problems

One of the best ways to learn statistics is to solve practice problems. These problems test your understanding of statistics terminology and your ability to solve common statistics problems. Each problem includes a step-by-step explanation of the solution.

  • Use the dropdown boxes to describe the type of problem you want to work on.
  • click the Submit button to see problems and solutions.

Main topic:

Problem description:

In one state, 52% of the voters are Republicans, and 48% are Democrats. In a second state, 47% of the voters are Republicans, and 53% are Democrats. Suppose a simple random sample of 100 voters are surveyed from each state.

What is the probability that the survey will show a greater percentage of Republican voters in the second state than in the first state?

The correct answer is C. For this analysis, let P 1 = the proportion of Republican voters in the first state, P 2 = the proportion of Republican voters in the second state, p 1 = the proportion of Republican voters in the sample from the first state, and p 2 = the proportion of Republican voters in the sample from the second state. The number of voters sampled from the first state (n 1 ) = 100, and the number of voters sampled from the second state (n 2 ) = 100.

The solution involves four steps.

  • Make sure the sample size is big enough to model differences with a normal population. Because n 1 P 1 = 100 * 0.52 = 52, n 1 (1 - P 1 ) = 100 * 0.48 = 48, n 2 P 2 = 100 * 0.47 = 47, and n 2 (1 - P 2 ) = 100 * 0.53 = 53 are each greater than 10, the sample size is large enough.
  • Find the mean of the difference in sample proportions: E(p 1 - p 2 ) = P 1 - P 2 = 0.52 - 0.47 = 0.05.

σ d = sqrt{ [ P1( 1 - P 1 ) / n 1 ] + [ P 2 (1 - P 2 ) / n 2 ] }

σ d = sqrt{ [ (0.52)(0.48) / 100 ] + [ (0.47)(0.53) / 100 ] }

σ d = sqrt (0.002496 + 0.002491) = sqrt(0.004987) = 0.0706

z p 1 - p 2 = (x - μ p 1 - p 2 ) / σ d = (0 - 0.05)/0.0706 = -0.7082

Using Stat Trek's Normal Distribution Calculator , we find that the probability of a z-score being -0.7082 or less is 0.24.

Therefore, the probability that the survey will show a greater percentage of Republican voters in the second state than in the first state is 0.24.

See also: Difference Between Proportions

Michigan Technological University

What is Business Statistics and How Can It Improve Organizational Efficiency?

What is Business Statistics and How Can It Improve Organizational Efficiency?

Most of us use the word “statistics” with apparent familiarity. We may know enough about the basics of statistics to understand a baseball player’s batting average or read a news report about political polls. However, any effort to answer the question, “What is business statistics?” requires a deeper understanding of statistics.

In short, when seeking to understand business statistics or statistical techniques in business and economics, we must operate from a foundation of the more precise methodology of statistics . Broadly speaking, this means dealing with “gathering, selecting, and classifying data; interpreting and analyzing data; and deriving and evaluating the validity and reliability of conclusions based on data.”

Applying the science of statistics to the more specific field of statistical analysis for business is more crucial than ever in an era heavily defined and driven by data. Businesses and organizations are constantly working to make sense of and utilize much of the 2.5 quintillion bytes of data being generated worldwide each day. The only way for any business to do that effectively is to rely on statisticians trained in statistical techniques in business and economics.

What Is Business Statistics?

The most simple and straightforward answer to this question is that business statistics is the application of standard statistical methods in business environments. The application of statistical techniques in business and economics includes and builds on most of the basic concepts in the field of statistics, including:

  • Bell Curves
  • Basic Probability
  • Hypothesis Testing
  • Regression Analysis

How Do Businesses Use Statistics And What Are Common Statistical Techniques In Business and Economics?

How Do Businesses Use Statistics And What Are Common Statistical Techniques In Business and Economics?

The use of statistical analysis for business takes on many forms in a wide variety of contexts. Businesses use statistics to project sales numbers, evaluate production methods, develop short- and long-term strategies, build and adjust organizational structure, and much more.

In fact, there are few areas of business that are not affected by whether or not statistics are understood and utilized effectively. Here are some of the most crucial applications of statistical techniques in business and economics.

Financial Data Management

Of course, any business relies on financial health for its ongoing viability. The most familiar aspect of financial maintenance for businesses is bookkeeping and accounting. Certainly expertise in those areas helps any business stay organized and efficient in the use of its resources.

However, the long-term security and growth of a company also depend on making wise use of the volumes of data going into and coming out of the accounting process. Financial data management is one of the key statistical techniques in business and economics. It makes use of various tools and methodologies to record, track, and make use of a company’s financial information, including :

  • Specialized Software
  • Data Visualization Tools

It is crucial for any company to not only accurately track but also effectively apply financial information. This is how healthy budgets are developed, reliable financial projections are made, and accurate records are kept for legal and compliance purposes. Financial data management is one of the most important tools of statistical analysis for business.

Measuring Organizational Performance and Efficiency

Measuring Organizational Performance and Efficiency

Another important example of the use of statistical techniques in business and economics for companies is assessing various areas of performance . Certainly the bottom lines of profit and loss can tell the most basic story about how a business is faring. However, there are many other crucial indicators that can only be examined and understood through business statistics.

For example, any company that produces physical products will have to perform product testing. Especially in cases of mass production, it simply is not possible to physically examine every item coming off of increasingly automated assembly lines. So, one effective use of statistical analysis for business is in product sampling, which estimates overall production quality by analyzing a designated segment of products.

Companies also employ various statistical tools to gauge the overall efficiency of their workforce and of their organizational structure. This can include measurements of employee output, examinations of time utilization, and other areas vital to maximizing the resources of the business. The data gathered can then be examined by statisticians to measure performance and efficiency and create models for improvement.

Improving Business Operations and Decision-Making

Improving Business Operations and Decision-Making

As tools of statistical analysis for business are used to measure efficiency and performance, companies can further utilize those statistics to make improvements in their operations and decision-making . Once again, this application is relevant in many sectors of a business, including:

  • Sales Analysis and Growth
  • Identifying More Effective Marketing Strategies
  • Better Employee Training and Management
  • Leadership Development for Executives
  • Improved Staffing and Scheduling Efficiency
  • More Effective Organizational Goal-Setting

If sales are lagging, a statistician employing statistical techniques in business and economics can help isolate the problem and identify solutions. For example, the analysis might indicate ineffective marketing, which then can be remedied through more effective market research and analysis, better projections, and improved messaging and advertising strategies.

Even issues that seem more personal and less likely to be remedied by statistics often can be addressed through creative use of statistical analysis for business. For example, if employee efficiency or even morale seems low, statistics can help identify inefficient business practices or trends unnecessarily burdening the staff. Addressing these issues not only helps improve business performance but also communicates concern and value to a company’s primary resource: its people.

Enhanced understanding and utilization of business statistics also equip senior company leadership . Many businesses find early success because of the entrepreneurial instincts of an individual or group but eventually struggle in new seasons and when facing new challenges. Early methodologies and decision-making processes don’t always translate as a business enters new stages. Often the application of statistical techniques in business and economics under the guidance of statisticians can help leaders overcome these barriers and make better decisions for the company’s present and future.

Identifying Trends And Improving Forecasting

Identifying Trends And Improving Forecasting

While nurturing internal health and efficiency is essential for any business, identifying and understanding external trends is also key. What was effective and relevant in a given industry last month may no longer be effective and relevant in six months. Statistical techniques in business and economics can help a company track and analyze trends. This has numerous benefits , such as:

  • Remaining Current
  • Knowing What to Expect Next
  • Generating Better Business Ideas
  • Creating Opportunities to Lead Rather Than Follow
  • Improving a Company’s Forecasting Capacities
  • Offering Early Warning Signs for Potential Problem Areas
  • Identifying Areas for Needed Improvement

Statisticians can help businesses make sense of industry trends and create solutions to adapt and thrive in changing environments. This includes using statistics to make better projections about how those environments are likely to continue to change. Improved forecasting in areas like sales, expenses, profit margins, production capacities, and product quality can determine whether a business’s best days are behind it or ahead of it.

Improving Market Research, Market Analytics, and Marketing

Market research is another area in which companies find their needs expanding as the business grows. An initial successful idea rarely sustains a business long term, so market research and analytics become the most important areas of statistical analysis for businesses.

In essence, this means using statistics to better understand potential product demand and to maximize return on product investment. This is a challenge for most businesses. In fact, a survey of senior marketing executives by the Harvard Business Review found that "more than 80% of respondents were dissatisfied with their ability to measure marketing ROI." This creates enormous opportunities for statisticians who can work with executives to solve that problem.

Ultimately, statistical techniques in business and economics are also a cornerstone for effective marketing campaigns. This is especially true in the era of Big Data, when businesses are inundated with internet-driven information. This influx of data can either overwhelm companies or offer unprecedented opportunities for targeted, data-informed marketing strategies. Trained statisticians are the difference-makers who can provide the tools and insights needed to maximize marketing efforts.

Job Opportunities And Earning Potential For Statisticians In Business

Job Opportunities And Earning Potential For Statisticians In Business

If you are interested in a career applying statistical techniques in business and economics or working on statistical analysis for business, there are many exciting opportunities available. In fact, U.S. News and World Report named the Statistician #1 in its list of Best Business Jobs, and #6 in both its overall list of 100 Best Jobs and its list of Best Science, Technology, Engineering, and Math (STEM) Jobs.

Job Outlook for Statisticians

One of the reasons the field of business statistics is rated so highly is the obvious understanding of its increasing importance in years to come. In fact, job opportunities for statisticians are growing at more than six times the rate for all jobs in the United States. Demand for trained statisticians is expected to increase by 31 percent by the year 2028.

This reality extends into other fields where trained statisticians are likely to thrive. Data science is an emerging field interrelated with statistics where many statisticians find work. Glassdoor lists Data Scientist #3 on its “50 Best Jobs for America in 2020.”

Salary Expectations for Statisticians

Another attractive dynamic of a career in business statistics is its high earnings potential. The Bureau of Labor Statistics lists the median salary for statisticians as $91,160 and indicates that the top ten percent of statisticians earn more than $146,770 ( source ).

With salaries near and well above six figures and an ever-expanding job market, trained statisticians are positioned to thrive for many years to come.

Are You Interested In A Career In Business Statistics?

Are You Interested In A Career In Business Statistics?

As businesses and executives continue to realize their need to effectively engage business statistics, the need for trained statisticians also continues to grow . Capable statisticians are increasingly at the center of business strategy, helping make sense of a never-ending stream of data and positioning companies for future growth.

Many of the best and most promising business opportunities for statisticians require a master’s degree. This level of education does far more than answer the question: “What is business statistics?” It equips future statisticians well beyond foundational understandings. Students receive training in industry-standard statistical and data analysis software, study datasets, learn data science, and gain experience solving real problems.

One of the best ways to create these kinds of opportunities for yourself is with an online Master’s in Applied Statistics from Michigan Tech.

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  • What is the best calculator for statistics?
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  • Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data. There are two main branches of statistics: descriptive statistics, and inferential statistics.
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  • Descriptive statistics is a branch of statistics that deals with summarizing, organizing and describing data. Descriptive statistics uses measures such as central tendency (mean, median, and mode) and measures of variability (range, standard deviation, variance) to give an overview of the data.
  • What is inferential statistics?
  • Inferential statistics is a branch of statistics that deals with making predictions and inferences about a population based on a sample of data. Inferential statistics uses probability theory and statistical models to make predictions and inferences about a population.
  • What is the difference between statistics and probability?
  • Statistics is the branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data, while probability is the branch of mathematics dealing with the likelihood of occurrence of different events.

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Statology

Statistics Made Easy

The Importance of Statistics in Business (With Examples)

The field of statistics is concerned with collecting, analyzing, interpreting, and presenting data.

In a business setting, statistics is important for the following reasons:

Reason 1 : Statistics allows a business to understand consumer behavior better using descriptive statistics.

Reason 2 : Statistics allows a business to spot trends using data visualization.

Reason 3 : Statistics allows a business to understand the relationship between different variables using regression models.

Reason 4 : Statistics allows a business to segment consumers into groups using cluster analysis. 

In the rest of this article, we elaborate on each of these reasons.

Reason 1: Understand Consumer Behavior Using Descriptive Statistics

Descriptive statistics are used to describe datasets.

Businesses in almost every field use descriptive statistics to gain a better understanding of how their consumers behave.

For example, a grocery store might calculate the following descriptive statistics:

  • The mean number of customers who come in each day.
  • The median sales order per customer.
  • The standard deviation of the age of the customers who come in the store.
  • The sum of the sales made each month.

Using these metrics, the store can gain a strong understanding of who their customers are and how they behave.

On the other hand, a bank might calculate the following descriptive statistics:

  • The percentage of customers who default on their loan.
  • The mean number of new customers who join the bank each day.
  • The sum of the total deposits made by all customers each month.

Using these metrics, the bank can get an idea of how their customers behave and how they handle their money.

Not all businesses build statistical models or perform complex calculations, but just about every business uses descriptive statistics to gain a better understanding of their customers.

Reason 2: Spot Trends Using Data Visualization

Another common way that statistics is used in business is through data visualizations such as line charts, histograms, boxplots, pie charts and other charts.

These types of charts are often used to help a business spot trends.

For example, a small business might create the following combo chart to visualize the number o f new clients and total sales they make each month:

business statistics problem solving

Using this simple chart, the business can quickly see that both their sales and number of new clients tends to increase the most in the final quarter of the year.

This can allow the business to be prepared with more staff, later hours, more inventory, etc. during this time of year.

Reason 3: Understand the Relationship Between Variables Using Regression Models

Another way that statistics is used in business settings is in the form of linear regression models .

These are models that allow a business to understand the relationship between one or more predictor variables and a response variable .

For example, a grocery store might track their total amount spent on print advertising, their total amount spent on online advertising, and their total revenue.

They might then build the following multiple linear regression model:

Sales = 840.35 + 2.55(TV advertising) + 4.87(online advertising)

Here’s how to interpret the regression coefficients in this model:

  • For each additional dollar spent on TV advertising, the total revenue increases by $2.55 (assuming online advertising is held constant).
  • For each additional dollar spent on online advertising, the total revenue increases by $4.87 (assuming TV advertising is held constant).

Using this model, the grocery store can quickly see that their money is better spent on online advertising as opposed to TV advertising.

Note : In this example, we only used two predictor variables (TV advertising and online advertising), but in practice businesses often build regression models with far more predictor variables.

Reason 4: Segment Consumers into Groups Using Cluster Analysis

Another way that statistics is used in business settings is in the form of cluster analysis .

This is a machine learning technique that allows a business to group together similar people based on different attributes.

Retail companies often use clustering to identify groups of households that are similar to each other.

For example, a retail company may collect the following information on households:

  • Household income
  • Household size
  • Head of household Occupation
  • Distance from nearest urban area

They can then feed these variables into a clustering algorithm to perhaps identify the following clusters:

  • Cluster 1: Small family, high spenders
  • Cluster 2: Larger family, high spenders
  • Cluster 3: Small family, low spenders
  • Cluster 4: Large family, low spenders

The company can then send personalized advertisements or sales letters to each household based on how likely they are to respond to specific types of advertisements.

Additional Resources

The following articles explain the importance of statistics in other fields:

The Importance of Statistics in Economics The Importance of Statistics in Education The Importance of Statistics in Healthcare

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Hey there. My name is Zach Bobbitt. I have a Masters of Science degree in Applied Statistics and I’ve worked on machine learning algorithms for professional businesses in both healthcare and retail. I’m passionate about statistics, machine learning, and data visualization and I created Statology to be a resource for both students and teachers alike.  My goal with this site is to help you learn statistics through using simple terms, plenty of real-world examples, and helpful illustrations.

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Artificial intelligence in strategy

Can machines automate strategy development? The short answer is no. However, there are numerous aspects of strategists’ work where AI and advanced analytics tools can already bring enormous value. Yuval Atsmon is a senior partner who leads the new McKinsey Center for Strategy Innovation, which studies ways new technologies can augment the timeless principles of strategy. In this episode of the Inside the Strategy Room podcast, he explains how artificial intelligence is already transforming strategy and what’s on the horizon. This is an edited transcript of the discussion. For more conversations on the strategy issues that matter, follow the series on your preferred podcast platform .

Joanna Pachner: What does artificial intelligence mean in the context of strategy?

Yuval Atsmon: When people talk about artificial intelligence, they include everything to do with analytics, automation, and data analysis. Marvin Minsky, the pioneer of artificial intelligence research in the 1960s, talked about AI as a “suitcase word”—a term into which you can stuff whatever you want—and that still seems to be the case. We are comfortable with that because we think companies should use all the capabilities of more traditional analysis while increasing automation in strategy that can free up management or analyst time and, gradually, introducing tools that can augment human thinking.

Joanna Pachner: AI has been embraced by many business functions, but strategy seems to be largely immune to its charms. Why do you think that is?

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Yuval Atsmon: You’re right about the limited adoption. Only 7 percent of respondents to our survey about the use of AI say they use it in strategy or even financial planning, whereas in areas like marketing, supply chain, and service operations, it’s 25 or 30 percent. One reason adoption is lagging is that strategy is one of the most integrative conceptual practices. When executives think about strategy automation, many are looking too far ahead—at AI capabilities that would decide, in place of the business leader, what the right strategy is. They are missing opportunities to use AI in the building blocks of strategy that could significantly improve outcomes.

I like to use the analogy to virtual assistants. Many of us use Alexa or Siri but very few people use these tools to do more than dictate a text message or shut off the lights. We don’t feel comfortable with the technology’s ability to understand the context in more sophisticated applications. AI in strategy is similar: it’s hard for AI to know everything an executive knows, but it can help executives with certain tasks.

When executives think about strategy automation, many are looking too far ahead—at AI deciding the right strategy. They are missing opportunities to use AI in the building blocks of strategy.

Joanna Pachner: What kind of tasks can AI help strategists execute today?

Yuval Atsmon: We talk about six stages of AI development. The earliest is simple analytics, which we refer to as descriptive intelligence. Companies use dashboards for competitive analysis or to study performance in different parts of the business that are automatically updated. Some have interactive capabilities for refinement and testing.

The second level is diagnostic intelligence, which is the ability to look backward at the business and understand root causes and drivers of performance. The level after that is predictive intelligence: being able to anticipate certain scenarios or options and the value of things in the future based on momentum from the past as well as signals picked in the market. Both diagnostics and prediction are areas that AI can greatly improve today. The tools can augment executives’ analysis and become areas where you develop capabilities. For example, on diagnostic intelligence, you can organize your portfolio into segments to understand granularly where performance is coming from and do it in a much more continuous way than analysts could. You can try 20 different ways in an hour versus deploying one hundred analysts to tackle the problem.

Predictive AI is both more difficult and more risky. Executives shouldn’t fully rely on predictive AI, but it provides another systematic viewpoint in the room. Because strategic decisions have significant consequences, a key consideration is to use AI transparently in the sense of understanding why it is making a certain prediction and what extrapolations it is making from which information. You can then assess if you trust the prediction or not. You can even use AI to track the evolution of the assumptions for that prediction.

Those are the levels available today. The next three levels will take time to develop. There are some early examples of AI advising actions for executives’ consideration that would be value-creating based on the analysis. From there, you go to delegating certain decision authority to AI, with constraints and supervision. Eventually, there is the point where fully autonomous AI analyzes and decides with no human interaction.

Because strategic decisions have significant consequences, you need to understand why AI is making a certain prediction and what extrapolations it’s making from which information.

Joanna Pachner: What kind of businesses or industries could gain the greatest benefits from embracing AI at its current level of sophistication?

Yuval Atsmon: Every business probably has some opportunity to use AI more than it does today. The first thing to look at is the availability of data. Do you have performance data that can be organized in a systematic way? Companies that have deep data on their portfolios down to business line, SKU, inventory, and raw ingredients have the biggest opportunities to use machines to gain granular insights that humans could not.

Companies whose strategies rely on a few big decisions with limited data would get less from AI. Likewise, those facing a lot of volatility and vulnerability to external events would benefit less than companies with controlled and systematic portfolios, although they could deploy AI to better predict those external events and identify what they can and cannot control.

Third, the velocity of decisions matters. Most companies develop strategies every three to five years, which then become annual budgets. If you think about strategy in that way, the role of AI is relatively limited other than potentially accelerating analyses that are inputs into the strategy. However, some companies regularly revisit big decisions they made based on assumptions about the world that may have since changed, affecting the projected ROI of initiatives. Such shifts would affect how you deploy talent and executive time, how you spend money and focus sales efforts, and AI can be valuable in guiding that. The value of AI is even bigger when you can make decisions close to the time of deploying resources, because AI can signal that your previous assumptions have changed from when you made your plan.

Joanna Pachner: Can you provide any examples of companies employing AI to address specific strategic challenges?

Yuval Atsmon: Some of the most innovative users of AI, not coincidentally, are AI- and digital-native companies. Some of these companies have seen massive benefits from AI and have increased its usage in other areas of the business. One mobility player adjusts its financial planning based on pricing patterns it observes in the market. Its business has relatively high flexibility to demand but less so to supply, so the company uses AI to continuously signal back when pricing dynamics are trending in a way that would affect profitability or where demand is rising. This allows the company to quickly react to create more capacity because its profitability is highly sensitive to keeping demand and supply in equilibrium.

Joanna Pachner: Given how quickly things change today, doesn’t AI seem to be more a tactical than a strategic tool, providing time-sensitive input on isolated elements of strategy?

Yuval Atsmon: It’s interesting that you make the distinction between strategic and tactical. Of course, every decision can be broken down into smaller ones, and where AI can be affordably used in strategy today is for building blocks of the strategy. It might feel tactical, but it can make a massive difference. One of the world’s leading investment firms, for example, has started to use AI to scan for certain patterns rather than scanning individual companies directly. AI looks for consumer mobile usage that suggests a company’s technology is catching on quickly, giving the firm an opportunity to invest in that company before others do. That created a significant strategic edge for them, even though the tool itself may be relatively tactical.

Joanna Pachner: McKinsey has written a lot about cognitive biases  and social dynamics that can skew decision making. Can AI help with these challenges?

Yuval Atsmon: When we talk to executives about using AI in strategy development, the first reaction we get is, “Those are really big decisions; what if AI gets them wrong?” The first answer is that humans also get them wrong—a lot. [Amos] Tversky, [Daniel] Kahneman, and others have proven that some of those errors are systemic, observable, and predictable. The first thing AI can do is spot situations likely to give rise to biases. For example, imagine that AI is listening in on a strategy session where the CEO proposes something and everyone says “Aye” without debate and discussion. AI could inform the room, “We might have a sunflower bias here,” which could trigger more conversation and remind the CEO that it’s in their own interest to encourage some devil’s advocacy.

We also often see confirmation bias, where people focus their analysis on proving the wisdom of what they already want to do, as opposed to looking for a fact-based reality. Just having AI perform a default analysis that doesn’t aim to satisfy the boss is useful, and the team can then try to understand why that is different than the management hypothesis, triggering a much richer debate.

In terms of social dynamics, agency problems can create conflicts of interest. Every business unit [BU] leader thinks that their BU should get the most resources and will deliver the most value, or at least they feel they should advocate for their business. AI provides a neutral way based on systematic data to manage those debates. It’s also useful for executives with decision authority, since we all know that short-term pressures and the need to make the quarterly and annual numbers lead people to make different decisions on the 31st of December than they do on January 1st or October 1st. Like the story of Ulysses and the sirens, you can use AI to remind you that you wanted something different three months earlier. The CEO still decides; AI can just provide that extra nudge.

Joanna Pachner: It’s like you have Spock next to you, who is dispassionate and purely analytical.

Yuval Atsmon: That is not a bad analogy—for Star Trek fans anyway.

Joanna Pachner: Do you have a favorite application of AI in strategy?

Yuval Atsmon: I have worked a lot on resource allocation, and one of the challenges, which we call the hockey stick phenomenon, is that executives are always overly optimistic about what will happen. They know that resource allocation will inevitably be defined by what you believe about the future, not necessarily by past performance. AI can provide an objective prediction of performance starting from a default momentum case: based on everything that happened in the past and some indicators about the future, what is the forecast of performance if we do nothing? This is before we say, “But I will hire these people and develop this new product and improve my marketing”— things that every executive thinks will help them overdeliver relative to the past. The neutral momentum case, which AI can calculate in a cold, Spock-like manner, can change the dynamics of the resource allocation discussion. It’s a form of predictive intelligence accessible today and while it’s not meant to be definitive, it provides a basis for better decisions.

Joanna Pachner: Do you see access to technology talent as one of the obstacles to the adoption of AI in strategy, especially at large companies?

Yuval Atsmon: I would make a distinction. If you mean machine-learning and data science talent or software engineers who build the digital tools, they are definitely not easy to get. However, companies can increasingly use platforms that provide access to AI tools and require less from individual companies. Also, this domain of strategy is exciting—it’s cutting-edge, so it’s probably easier to get technology talent for that than it might be for manufacturing work.

The bigger challenge, ironically, is finding strategists or people with business expertise to contribute to the effort. You will not solve strategy problems with AI without the involvement of people who understand the customer experience and what you are trying to achieve. Those who know best, like senior executives, don’t have time to be product managers for the AI team. An even bigger constraint is that, in some cases, you are asking people to get involved in an initiative that may make their jobs less important. There could be plenty of opportunities for incorpo­rating AI into existing jobs, but it’s something companies need to reflect on. The best approach may be to create a digital factory where a different team tests and builds AI applications, with oversight from senior stakeholders.

The big challenge is finding strategists to contribute to the AI effort. You are asking people to get involved in an initiative that may make their jobs less important.

Joanna Pachner: Do you think this worry about job security and the potential that AI will automate strategy is realistic?

Yuval Atsmon: The question of whether AI will replace human judgment and put humanity out of its job is a big one that I would leave for other experts.

The pertinent question is shorter-term automation. Because of its complexity, strategy would be one of the later domains to be affected by automation, but we are seeing it in many other domains. However, the trend for more than two hundred years has been that automation creates new jobs, although ones requiring different skills. That doesn’t take away the fear some people have of a machine exposing their mistakes or doing their job better than they do it.

Joanna Pachner: We recently published an article about strategic courage in an age of volatility  that talked about three types of edge business leaders need to develop. One of them is an edge in insights. Do you think AI has a role to play in furnishing a proprietary insight edge?

Yuval Atsmon: One of the challenges most strategists face is the overwhelming complexity of the world we operate in—the number of unknowns, the information overload. At one level, it may seem that AI will provide another layer of complexity. In reality, it can be a sharp knife that cuts through some of the clutter. The question to ask is, Can AI simplify my life by giving me sharper, more timely insights more easily?

Joanna Pachner: You have been working in strategy for a long time. What sparked your interest in exploring this intersection of strategy and new technology?

Yuval Atsmon: I have always been intrigued by things at the boundaries of what seems possible. Science fiction writer Arthur C. Clarke’s second law is that to discover the limits of the possible, you have to venture a little past them into the impossible, and I find that particularly alluring in this arena.

AI in strategy is in very nascent stages but could be very consequential for companies and for the profession. For a top executive, strategic decisions are the biggest way to influence the business, other than maybe building the top team, and it is amazing how little technology is leveraged in that process today. It’s conceivable that competitive advantage will increasingly rest in having executives who know how to apply AI well. In some domains, like investment, that is already happening, and the difference in returns can be staggering. I find helping companies be part of that evolution very exciting.

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