T-test and Hypothesis Testing (Explained Simply)

Student’s t-tests are commonly used in inferential statistics for testing a hypothesis on the basis of a difference between sample means. However, people often misinterpret the results of t-tests, which leads to false research findings and a lack of reproducibility of studies. This problem exists not only among students. Even instructors and “serious” researchers fall into the same trap. To prove my words, I can link <a href="https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.0020124" rel="noopener ugc nofollow" target="_blank">this article</a>, but there are others. Another problem is that I’ve often seen and heard complaints from some students that their teachers don’t explain the concept of t-tests sufficiently. Instead, they focus on calculations and interpretation of the results. Nowadays, scientists use computers to calculate t-statistic automatically, so there is no reason to drill the usage of formulas and t-distribution tables, except for the purpose of understanding how it works. As for interpretation, there is nothing wrong with it, although without comprehension of the concept it may look like blindly following the rules. Actually, it is. Do you remember? <a href="https://towardsdatascience.com/t-test-and-hypothesis-testing-explained-simply-1cff6358633e">Click Here</a>