How to Avoid P Hacking

“There are three kinds of lies: Lies, Damned Lies and Statistics,” Benjamin Disraeli. Much of this distrust around statisticians has to do with p-hacking. A p-hacker is someone who violates the rules of statistics by influencing the data collection process or statiscal analyses performed, in order to produce a statistically significant result.

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Timestamps
- 00:00
- 1:10 - Tip 1 Establish hypothesis and sample size before data collection
- 1:18 - Tip 2 Avoid multiple tests on the same data set over time
- 1:24 - Tip 3 Accounting for number of comparisons
- 1:31 - Tip 4 Don’t choose a subset of data to analyse after observing the results
- 1:35 - Tip 5 Don’t eliminate outliers without identifying a special cause
- 1:40 Tip 6 Don’t change the alt. Hypothesis to match the direction of the results observed
- 1:45 Tip 7 Don’t choose a less appropriate analysis because the p-value shows you the result you want
-1:52 P-Hacking in the real world


About the video
Join Jim Colton, M.S., Statistics and the Lead Statistician at Graphpad, as explores the importance of this topic. P-hacking is significant for all researchers because the statistical results can only be interpreted at face value, when every choice in data analysis was performed exactly as planned and documented as part of the experimental design.

In this video you will learn:
- What P-hacking is and why it’s important
- Seven steps for avoiding P-hacking
- How P-hacking can occur (real-world example)

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Видео How to Avoid P Hacking канала GraphPad Software