Lazy Bayesian

Regardless of the statistical test that you are using, the process of rejecting or retaining a null hypothesis can be confusing for many. I'm not going to target any one hypothesis test, rather discuss the general logic. My intention with this post is to provide students of introductory statistics courses (or anyone attempting to learn these concepts) some additional insight into how to understand and interpret hypothesis tests. Whether you are conducting a t-test, F-test, chi-square, or are testing regression coefficients from a model, the general idea behind it all is the same. All statistical hypothesis tests follow the same general approach of testing the scenario of the null hypothesis. That is, there is no association or detectable effect with your outcome variable, also known as the dependent variable. The alternative hypothesis is usually the research hypothesis, e.g. soda affects obesity, or excessive exposure to business meetings is associated with reduced brain funct

This online statistics tutor lesson is intended to supplement introductory statistics material as additional instruction and review. In this lesson we will only be covering beginning concepts regarding confidence intervals around an estimated mean. Estimating confidence intervals uses essentially the same principles and concepts used for calculating z-scores and normal probabilities (at least for CIs for means estimated from normal data). If you need a refresher regarding these concepts, check out one of my other posts . Uncertainty in Research and Statistics Though many are reluctant to admit it, there is a great deal of uncertainty in the information that we consume. Information sources (including legitimate sources) boast new conclusions about the world around us from healthy eating and everyday behavior to climate change and astrophysics. Something that many media sources often glaze over is that NONE of them are 100% sure about their hypothesized conclusion . These conclusion