: p-value in statistics
In: 10
The p-value is the chance of a result to be due to statistical chance/randomness alone.
It is measured on a scale from 1 = 100% to 0 = 0%. 0.02 would be 2%. 0.002 = 0.2% etc.
Application:
1)”Eating tomatoes causes lung cancer (p-value: 0.8)”
That means, there is a 80% chance of a observing a result like this if we just assumed randomness/coincidence. Hence, the result seems pretty unconvincing.
2) “Smoking causes lung cancer (p-value: 0.008)”
That means there is a 0,8% chance of obersving a results like this if we assumed randomness/coinicidence. This is way more convincing.
EDIT:
As the comments rightfully pointed out, this is a simplified case where we assume the null hypothesis to be randomness. This is the most common null hypothesis by a margin. However, it’s true that the null hypothesis could be different. A more precise definition, I enjoy: “The P value means the probability, for a given statistical model that, when the null hypothesis is true, the statistical summary would be equal to or more extreme than the actual observed results” ([Source](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5665734/#B2)). It’s just not very 5-year old friendly….
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