Statistics is all about understanding, analyzing, and deriving value from data. As straightforward and important as it sounds, becoming proficient at using statistics as a tool is not without challenges.
And the (known) truth that it is easy to make mistakes—at various steps during analysis—that lead to incorrect conclusions doesn’t help either. This guide goes over five such common mistakes in statistical analysis so you can steer clear of them.
Though helpful for beginners who likely overlook these common pitfalls, you can review these tips even if you’re an experienced data professional. Let’s begin.
1. Don’t Confuse Correlation with Causation
One of the most fundamental errors in statistical analysis is interpreting correlation as causation. It’s often possible that two variables change together. Which leads us to believe that the change in one variable is likely caused by the other. Misinterpreting correlation as causation can lead to possibly incorrect conclusions.
When you observe a correlation, always ask yourself why these variables might be related. Is the relationship relevant and rational or could there be a latent variable influencing both?
Encourage critical thinking by exploring different hypotheses. Don’t just accept a correlation at face value—consider alternative explanations, and remember that correlation might simply be a coincidence, especially in large datasets with many variables.
When reporting findings, make it clear that correlation does not imply causation. This helps set the right expectations for both teammates and stakeholders who might otherwise misinterpret your results.
2. Understand the Assumptions of Statistical Tests
Every statistical test is built on a set of assumptions. If these assumptions are not met, the results of the test can be misleading. For instance, a t-test assumes that the data is normally distributed and that variances between groups are equal.
Before applying a statistical test, take the time to understand its requirements and check whether your data meets these criteria. For example, use diagnostic tools to check for normality or equal variance.
If your data doesn’t meet the assumptions of a parametric test, consider non-parametric alternatives. These tests are more flexible and do not require strict adherence to assumptions like normality.
3. Ensure Adequate Sample Size and Statistical Power
From the Law of Large Numbers, we know that as the sample size increases, the sample mean converges to the population mean. However, with small samples, the estimates can be highly variable and unreliable.
Inadequate sample size reduces the statistical power of your analysis, increasing the risk of Type II errors (failing to detect a true effect). Before collecting data, conduct a power analysis to determine the necessary sample size to detect a meaningful effect.
Even if your analysis is statistically significant, consider the practical significance. With very large samples, even trivial effects can become statistically significant, leading to misleading interpretations.
When publishing or presenting your results, be transparent about your sample size and the power of your study. Also be sure to acknowledge any limitations this might impose on the generalizability of your findings.
4. Correct for Multiple Comparisons to Avoid False Positives
It’s quite common to perform multiple statistical tests. But when doing so, you’ll likely find a statistically significant result (incorrectly by chance). This can lead to false positives, where you mistakenly believe there is an effect when there is none.
When your analysis involves multiple comparisons, such as testing several hypotheses or performing multiple subgroup analyses, adjust your significance levels to account for this.
It helps to pre-plan your analysis strategy, including how you will handle multiple comparisons. This reduces the temptation to engage in “p-hacking”, where analysts try multiple approaches until they find a significant result.
5. Understand and Mitigate Type I and Type II Errors
Type I errors (false positives) occur when you wrongly reject a true null hypothesis, while Type II errors (false negatives) happen when you fail to reject a false null hypothesis. Both errors can significantly impact the validity of your analysis and the decisions based on it.
Choosing an appropriate significance level (alpha) is important. Setting a lower alpha reduces the risk of Type I errors but increases the risk of Type II errors, and vice versa. Consider the context of your analysis and the consequences of each type of error when setting your significance level.
The cost of making a Type I error versus a Type II error can vary depending on the field of study. For example, in medical research, a Type I error might lead to approving an ineffective treatment, while a Type II error could mean missing out on a life-saving therapy. Always weigh these risks carefully in your analysis.
Conclusion
Avoiding common statistical mistakes requires a good mix of knowledge and critical thinking. Keep these tips in mind as you work with data so you can analyze your data better and make more reliable inferences.
If you’re interested in learning statistics for data science, read 7 Steps to Mastering Statistics for Data Science. Happy learning!
