Completing the Hypothesis Testing in Python course has enhanced my understanding of the one-sample proportion test workflow. Along the way, I explored key concepts such as z-scores, p-values, and Type I and Type II errors. Below is a summary of my learning journey:
Hypothesis Tests and Z-Scores – I examined a real-world case where hypothesis testing played a crucial role in decision-making.
Two-Sample and ANOVA Tests – I learned how to compare means between two groups using t-tests and extended this to multiple groups using ANOVA and pairwise t-tests.
Proportion Tests – Through hands-on exercises, I applied chi-square independence tests to compare proportions across multiple groups and revisited the one-sample case with chi-square goodness-of-fit tests.
Non-Parametric Tests – I explored the assumptions underlying parametric tests and learned how non-parametric tests serve as alternatives when those assumptions are violated.
Check here for details.