Understanding P-Values: Why significant results do not mean meaningful results

P values are the most widely used metric of significance in statistical analyses. However, they are often misused and misunderstood. This webinar will break down precisely what a p-value is, how to interpret it correctly, and why a significant p-value does not equate to meaningful results.

Presented by Dillon Jones (@BiologistDillon)

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0:00 intro
0:36 What is a p value?
1:40 Example - Probability of a coin flip
3:26 What is Hypothesis testing?
5:41 Example - Size of frogs between 2 populations
6:24 As P Values Decrease probability of Incorrect Conclusions Decrease & example
8:59 how to detect difference in trend not the magnitude & example
10:34 Biologically Meaningful vs Statistically Meaningful?
11:08 Example - Significance between weight of lizard populations
12:40 How to measure effect size?
15:27 what is Difference of means?
16:35 What is Cohen's D?
18:40 What is correlation and R^2?
21:13 What are 5 pitfalls of P Values?
25:42 Q&A