Exact and randomization tests are simple from a conceptual level and need fewer assumptions than traditional parametric tests. They do require substantial computing power, but nothing that can’t be handled by the computer you have today. (more…)
Choosing a Statistical Test
Member Training: Exact and Randomization Tests
June 2nd, 2023 by guest contributerA Post-hoc Test for Kruskal-Wallis
May 8th, 2023 by guest contributerIf you’ve ever run a one-way analysis of variance (ANOVA), you’re familiar with post-hoc tests. The ANOVA omnibus test only tells you whether any groups differ in their means. But if you want to explore which specific group mean is different from which, you need to follow up with a post-hoc test. (more…)
What is the Mann-Whitney U Test?
April 13th, 2023 by Kim LoveWhen you need to compare a numeric outcome for two groups, what analysis do you think of first? Chances are, it’s the independent samples t-test. But that’s not the only, or always, the best option. In many situations, the Mann-Whitney U test is a better option.
The non-parametric Mann-Whitney U test is also called the Mann-Whitney-Wilcoxon test, or the Wilcoxon rank sum test. Non-parametric means that the hypothesis it’s testing is not about the parameter of a particular distribution.
It is part of a subgroup of non-parametric tests that are rank based. That means that the specific values of the outcomes are not important, only their order. In other words, we will be ranking the outcomes.
Like the t-test, this analysis tests whether two independent groups have similar typical outcomes. You can use it with numeric data, but unlike the t-test, it also works with ordinal data. Like the t-test, it is designed for comparisons, and not for estimation or prediction.
The biggest difference from the t-test is that it does not compare means. The Mann-Whitney U test determines whether a random observation from one group tends to be higher (or lower) than a random observation from the other group. Imagine choosing two observations, one from each group, over and over again. This test will determine whether one group is more likely to have the higher values.
It has many advantages: It is a straightforward comparison of means. There are versions for similar and different variances in the two groups. Many people are familiar with it.
Member Training: Analyzing Likert Scale Data
August 31st, 2022 by TAF Support
Is it really ok to treat Likert items as continuous? 
And can you just decide to combine Likert items to make a scale? Likert-type data is extremely common—and so are questions like these about how to analyze it appropriately. (more…)
When Linear Models Don’t Fit Your Data, Now What?
June 20th, 2022 by Karen Grace-MartinWhen your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, linear models don’t fit. The data just will not meet the assumptions of linear models. But there’s good news, other models exist for many types of dependent variables.
Today I’m going to go into more detail about 6 common types of dependent variables that are either discrete, bounded, or measured on a nominal or ordinal scale and the tests that work for them instead. Some are all of these.
Member Training: Classic Experimental Designs
October 29th, 2021 by Kim Love
Have you ever wondered why there are so many different types of experimental designs, and how a researcher would go about choosing among them to best address their research questions? (more…)