People Analytics: Why Statistics Is Not a Waste of Time | Analytics in HR

A lot of HR practitioners come into the field with a background in HRM studies or Industrial and organizational psychology. These studies rely heavily on teaching statistics to their students. As a student, it’s often hard to imagine why statistics are so important. Especially if you don’t want to become an academic researcher, statistics feels a bit of a waste of time. Most of us want to work with people and just ‘do’ HR. The relevance of statistics is missing.

However, as most fans of people analytics know, the application of statistics in HR is the foundation of what we call HR analytics. Understanding statistics, knowing how to look at data differently, and being able to analyze it when needed helps to make better decisions.

In fact, this is what I hear often from students of statistics. Nothing is more helpful in making better and evidence-based decisions than a solid understanding of the thing we base most of our conclusions on data and statistics.

Statistics for People Analytics

A lot of emphasis has been placed on aggregating data from multiple systems and creating dashboards filled with HR metrics. These are important steps to get analytics off the ground. Those of us who already use Excel, Power BI or R to visualize our data know the impact that this can have.

However, what happens to your conclusions and decisions if it turns out that the data you have are not representative? And what if you could easily check the quality and accuracy of your data, and easily remove false outliers that skew results? Having the ability to think systematically about data is essential for people analytics, and knowing how to check for correlations and cause and effect relationships gets to the core of people analytics.

Statistically significant outliers

Statistics is a big part of people analytics and it applies to various analyses. For example:

  • If most of your people perform ‘satisfactory’, how will you discriminate between good or bad performance? This is where a solid understanding of the spread of data and differentiating people in order to draw conclusions becomes indispensable.
  • Or, when you start an analytics project, do you keep in mind that data has a tendency to regress to its normal average? Analytics projects are often a response to a problem in the organization, but this problem could be caused by a one-time outlier in the data. This would mean that the next time we take a measurement, this outlier will be reduced to normal. This is called regression to the mean.
  • Another example is response rates of questionnaires. Did your engagement survey last time get an equal response rate between different groups in the organization? Or is this something you didn’t check? To know if some groups are over or underrepresented in your survey, you can use some relatively easy statistical techniques to check this.

For the readers amongst us, the importance of deliberate and systematic thinking about data is emphasized by Daniel Kahneman’s book Thinking Slow and Fast. We’re able to quickly process information as soon as we see it, but this is influenced by our biases and other emotions. Only by taking a more deliberative and logical approach, we will start to make more objective decisions. Students of statistics are better at this as they are aware of many of the fallacies that we’re susceptible for.

Teaching Statistics in HR

At AIHR we’ve made it our mission to advance people analytics in HR. We’ve done this through our blog and through the courses we offer at the HR Analytics Academy.

Creating a course is not easy – it requires a lot of work. That is why we are very careful in deciding which course we do want to make and which ones we don’t.

Ever since launching the HR Analyst and HR Analytics Leader course, we’ve had more and more people ask us about a deep dive into statistics. We talk about statistics already in the HR Analyst course. However, in this course we only scratch the surface of actual analytics. To explain statistics properly, we needed to create a full course.

This is what we’ve been working on very hard in the past few months. We’re very excited to announce the Statistics in HR course. This course will help you to take your analytics to the next level by diving deeper into statistical testing.

HR Analytics Intro Webinar

A course on statistics

In the course, we will cover multiple topics, split into four modules.

  1. Introduction to statistics
  2. Methodology
  3. Basic statistical tests
  4. Advanced statistical tests

In module 1, you will learn how statics affect your everyday life and how it can help us in HR. You will learn about simple techniques to understand data better, like the mean, mode, median, and range. You will learn about the spread of data, how this influences your data analysis, and finally, you will learn techniques to clean and visualize data. The first two modules prepare you for the actual analytics in module 3 and 4.

In module 2, you will learn more about methodology. Statistics and methodology go hand in hand. One cannot work without the other. In this module, you will learn about sampling, bias, probability, hypothesis testing, and conceptual models. These are key concepts to understand before you can accurately analyze your data.

In module 3 and 4, you will learn basic and advanced statistical tests. We will start with correlation analysis, t-test, and ANOVA. In the second part, you will learn about linear regression, multiple regression, logistic regression, and structural equation modeling.

We dedicate a full lesson to explaining each of these tests. We will explain how these tests work, when they can be applied, the core assumptions of these test, and we will link you to various resources to execute these tests in the software that you use.

Because these tests can be done using Excel, SPSS, R, or another statistical package, we focus the lessons on a step-by-step process of doing the test which is independent of the software you use. This means that you will have all the information to do this test regardless of whether you’re using R or SPSS.

Course instructors

The course has two instructors.

Bastiaan Stokkel graduated in Social and Organizational Psychology. After graduating he has taught statistics at Leiden University and has worked as curriculum manager at the AIHR Academy before moving into an HR analyst role at Robeco, an investment firm. In teaching this course, he uses his experience as a teacher and analyst to show how statistics can be applied to HR data in a practical way. His expertise on the application of statistics in HR makes Bastiaan one of the best instructors in this field.

Erik van Vulpen is the founder of AIHR. He graduated with honors in both psychology and business administration before founding AIHR. He often writes about people analytics, is a conference speaker and chair, and instructor for the AIHR academy. For this course, Erik created the bonus lessons which focus on the application of the techniques learned in daily practice.

The proof is in the pudding

The best proof is in the eating of the pudding. Below, you can find the first lesson of the course. In this lesson, Bastiaan first gives a general introduction to statistics and then applies what you will learn to HR!

Click this link to read more information about the Statistics in HR course.

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