I’ve been reading about how some studies that we assume provide us with information about the world actually don’t teach us anything because of statistical manipulation that makes them look more relevant than they are. How does this work?
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In statistical analysis we use a system of 2 hypotheses and probability values. The hypotheses are known as the null and alternate hypothesis. The null hypothesis is simply the statement that IF x is true then there is no pattern. The alternate says IF the null isn’t true then there may be a pattern. The p-value is simply the likelihood we place to the data we record. In theory the data we collect should follow a bell curve. Meaning low values and high values occur less frequently. Out p-value is the line we draw saying “if we have too x amount of our data above or below this, then there’s too much noise to call this a pattern. ”
Typically we set a p-value of .05. meaning that is 95% of all the data we collected falls within our expected range then the alternate hypothesis is true and there may be a pattern. If greater than 5% of the data we collected falls outside our expected range then there is no pattern.
P-hacking is setting an inappropriately high p-value or changing your p-value after the fact to reject your null hypothesis. Or it’s stopping the collection of data at an inappropriate time because your data is beginning to approach your p-value.
It’s academically dishonest and let’s you make claims about patterns that may not be present.
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