(2020) dataset used for the Repeated Measures ANOVA. It provides more uniform and user-friendly functions than SciPy and Statsmodels.Īll datasets used below are taken from the example data included with JASP, with the exception of the Zhou et al. Pingouin is a relatively new Python library for statistics. Statsmodels is powerful, but not very user-friendly therefore, the tutorial below shows examples of several commonly used statistical tests. More advanced statistical tests are provided by Statsmodels. It also contains statistical functions, but only for basic statistical tests (t-tests etc.). SciPy is a Python package with a large number of functions for numerical computing. Correlating activity in the left and right brain.

It looks like the means of your data are different, just not enough to be statistically unlikely under the null hypothesis. Would anyone be able to explain how this makes sense that the null hypothesis IS NOT rejected? I'm not sure what my gap is in my understanding of ANOVA. When you take the means of A, B, and C, they aren't equal to each other. The p-value is greater than alpha, so the null hypothesis IS NOT rejectedĪs far as I can tell, I implemented everything correctly. Print('The p-value is greater than alpha, so the null hypothesis IS NOT rejected') Print('The p-value is less than alpha, so the null hypothesis IS rejected.') Print('tstat = ', test_statistic, ', p-value = ', p_value) Test_statistic, p_value = f_oneway(A, B, C) 𝐻𝑎 : At least one of the mean times spent in line at Store B for A, B, and C is unequal.Ī = ī = Ĭ = 𝐻0 : The mean times spent in line at Store B for A, B, and C are equal. I believe this would be an ANOVA analysis and below is my thought process of how I implemented it in Python. The groups are A, B, and C, and I want to determine if those three groups' mean time spent in line at a store is equal or not. I am working with a sample of data for three different groups, and the times they spent in a line at Store B.

Scipy anova