Null hypothesis significance testing (NHST)
Channel: Mikko Rönkkö
Date: 2019-07-10

This talk covers the concept of null hypothesis significance testing (NHST). The premise of NHST is to start with defining an estimation problem that delivers an estimate and define a test statistic along with a null hypothesis. The null hypothesis typically asserts that there's no effect or correlation in the specific scenario being tested. The discussion moved onto an explanation of how to derive a reference distribution based on statistical theory, and how the calculation of a test statistic for comparison leads to the designation of a p-value.

Further, the lecture discussed the t-test, highlighting its assumption of normal distribution for estimates over repeated samples. It also elucidated how a t-test standardizes an estimate by using methods of statistical subtraction and division. The discussion also touched upon, the importance of comparing both ends of the tail - one-tailed and two-tailed tests and why two-tailed tests are preferable. It also explained the flawed ethical practice of switching from a two-tailed to a one-tailed test, highlighting the differences in p-values obtained from both. The lecture ended on a note stressing the ideal thresholds for p-values, the errors involved - false positives and negatives, and the use of statistical power to ascertain these thresholds. It ended with the assertion that the field should focus more on negating false positives due to their larger implications in real-world applications.

Link to the slides: https://osf.io/95z3t