If all the talk about P-values has you concerned, learn Estimation Stats
/P-values got you down? Move past them.
Below are the first couple of paragraphs from an exceptionally clear and helpful webpage that provides an introduction to estimation statistics. P-values are a simple, useful metric from a time when computation was labor intensive but we live in a more data-rich world now and it’s now possible to do statistics in a way that captures the parts we care about—like effect size.
ESTIMATION STATS / WHAT IS ESTIMATION STATS?
This site provides you with a web application to plot experimental data from an estimation statistics perspective. You may have found significance testing and P-values problematic; you may be asking what comes next.
Introducing Estimation Statistics
Estimation statistics is a simple framework that—while avoiding the pitfalls of significance testing—uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to significance testing.
Significance testing calculates the probability (the P value) that the experimental data would be observed, if the intervention did not produce a change in the metric measured (i.e. the null hypothesis). This leads analysts to apply a false dichotomy on the experimental intervention.
Estimation statistics, on the other hand, focuses on the magnitude of the effect (the effect size) and its precision. This encourages analysts to gain a deeper understanding of the metrics used, and how they relate to the natural processes being studied.
