KANDA DATA

If you’re conducting research to compare the means of more than two sample groups, one-way ANOVA is a commonly used statistical test. However, using this test comes with certain assumptions that must be met, specifically, that the data are normally distributed and homogenous.

But what should you do if one of these assumptions, such as the normal distribution of data, is not satisfied? This situation can certainly be challenging, especially when you’ve collected your data through considerable effort. In this article, Kanda Data will explore possible solutions when your dataset doesn’t meet the normality assumption.

Why Use One-Way ANOVA in the First Place?

Before diving deeper, let’s first understand the basic concept behind the test. You may wonder: why choose one-way ANOVA?

As mentioned earlier, one-way ANOVA is the appropriate test when you’re comparing the means of more than two sample groups. Normally, if you’re comparing two groups, you’d use a paired t-test or an independent t-test. But for more than two groups, one-way ANOVA is the recommended approach to determine whether significant differences exist among the group means.

Assumptions of One-Way ANOVA

To ensure that one-way ANOVA yields consistent and unbiased results, several assumptions must be met. Besides homogeneity of variance, a key assumption is the normality of data distribution. This is where a normality test comes in, it helps confirm whether your data are normally distributed.

There are several ways to assess normality. Two commonly used tests are the Shapiro-Wilk test and the Kolmogorov-Smirnov test. If the p-value is greater than 0.05, you can conclude that the data follow a normal distribution, fulfilling the assumption.

What If the Data Are Not Normally Distributed?

So, what can you do if your normality test shows that the data are not normally distributed? Even after trying to fix it, by detecting outliers, recollecting certain samples, or increasing the sample size, you might still find your data deviate from a normal distribution.

Don’t worry. If you’re in this situation, you don’t have to force your analysis to fit the assumptions of one-way ANOVA. Instead, you can use a non-parametric alternative that doesn’t require the normality assumption. A great alternative is the Kruskal-Wallis test.

This non-parametric test serves the same purpose as one-way ANOVA: to compare more than two sample groups. The main difference is that the Kruskal-Wallis test compares medians, not means. It’s also particularly suitable when your data are measured on an ordinal scale.

Conclusion

That concludes this article from Kanda Data. Hopefully, it provides useful insights and adds value to your understanding of statistical analysis. Stay tuned for more updates in future articles!