Some of the common misconceptions around statistics are, 'Statistics can't tell you anything about the why, only the how much,' or 'Statistics needs normal distributions, and we don't have normal distributions with real data' and 'If something isn't statistically significant you can ignore it – it doesn't matter.'
It's time to bust these myths and misconceptions so that HR can harness the full power of statistics in their people analytics function — functions which – by the way – have grown by 43% since 2020.
Therefore, in this article, we're going to answer the top six common questions we hear about the basics of statistics in HR:
Statistics is analysing, making sense of and using information to make informed judgments and decisions. In an HR context, that means using relevant HR data to make less biased, more objective decisions and recommendations, as opposed to relying on gut feel or intuition alone.
As with any people analytics activity, starting with the problem your organisation is trying to solve, for example, a high attrition rate, is critical. Statistics can help HR professionals understand and validate potential causes for this issue, making data-driven recommendations to stakeholders, and positioning themselves as strategic partners for the future of work.
As Ben Teusch outlines, the key is to really understand the question you're trying to answer by spending time answering 'pre-analysis' questions. Therefore, it's important to always start with one key question you are trying to answer for your organisation. This can involve writing hypotheses and then testing them to ensure clarity in problem-solving.
A common misconception is that 'advanced analytics modelling' means 'better' – this is simply not the case! Basic statistics can be just as powerful in helping you answer questions and address challenges in an evidence-based way, providing valuable insights into HR data.
Basic statistics refer to descriptive statistics in HR, such as the mean, median, and mode. These are measures of central tendency, which can be considered measures of the 'middle'. We also use measures of dispersion (how spread out things are), such as the range and standard deviation (how spread out information is from the average).
Basic statistical analysis can also help understand things such as:
On the other hand, advanced analytics includes more complex techniques. For instance, you have data science, which analyses unstructured data such as employee feedback or communication platform data, using techniques such as natural language processing (NLP) to understand sentiments and themes in the feedback.
And if we look into the immense potential of AI in HR, we see that generative AI and people analytics techniques like regression analysis, predictive modelling and machine learning can provide more accurate insights and predictions.
Take for instance, Tomas Chamorro-Premuzic article which explains how AI is better at predicting job potential than humans. In his words,
"AI can accurately translate the words we use into a reliable estimate of our personality, values, and intelligence, as well as identifying language patterns that signal narcissism."
However, just because you have access to advanced statistical techniques doesn't necessarily mean you should use them for every HR problem. The focus should always be on addressing the specific business problem at hand, using whatever method is best suited for that particular challenge.
For example, conducting the basic statistical analysis may be more appropriate for understanding correlations between employee engagement and performance in a retail store (see case study in question 6). Whereas using predictive modelling through AI and organisational network analysis (ONA) could be an effective approach for identifying high-potential employees in a large organisation.
This is why it is ever important in upskilling HR in understanding the distinctions between data analytics, data science, and machine learning and know when each should be applied to make the most of HR data and drive actionable outcomes.
Simply put, anyone looking to make sense of HR data and use that information for better decision-making can benefit from statistics in HR.
HRBP's and connector roles
People analytics and statistics can be a great asset for business partners (HRBPs) – and anyone who acts as a bridge between HR and the business, such as connectors - because it allows them to move beyond simply presenting information to having strategic conversations with stakeholders. This enables them to take an evidence-based approach to HR strategy and align it more closely with overall business strategy.
However, for HR to truly make evidence-based decisions, it's important in upskilling HR to consider multiple sources and types of evidence and information. As Rob Briner highlights in his People Management article on information integrations, this could include: stakeholders' views, perspectives and judgments; professional expertise of practitioners; data and evidence from the context or setting; and scientific findings.
Business leadership and people managers
Business leadership and people managers can also greatly benefit from statistics in HR. By providing a more objective and less biased understanding of their workforce, they can make better decisions to drive productivity and success.
As Lexy Martin, former research principal at Visier, shares on the Digital HR Leaders podcast,
"Providing data and insights to managers makes them more effective and more human. So, they're delivering cost savings and revenue improvement, they're preparing for the future, and they're balancing this need to have their organisations be both profitable and productive while also enabling their people and their teams to thrive."
Employees
And finally, the workforce itself can also benefit from statistics in HR. By using insights gathered through analytics modelling, organisations can improve employee experience and give a voice to employees by surfacing essential insights that were previously unseen. This can lead to more informed and influential people strategies that consider employee needs and well-being.
