The above is what happens in our case example. Additionally, it’s also expected that 1 out of 20 experiments is a false positive. If the null hypothesis is true, then it’s expected that 19 out of 20 experiments the scientists performed should have non-statistically significant difference. Since the people recovered without the drug for both groups were taken from the same population, there’s a high chance that their means should be close enough (fit for the starting point) every time we collect them. To ensure that the difference really exists, we expect that the mean between two group of people recovered without the drug doesn’t differ too much (for the sake of the same starting point). On the other hand, there’s a 5% chance that the mean difference is considered significant but not in reality (false positive / type I error). The sampling distribution of the mean difference has zero mean and 95% of the time we’d get the mean differences that are not statistically significant. The top reason behind this act is because researches with statistically significant results are more likely to be accepted. Performing experiments multiple times until getting the wanted result (without reporting lots of unwanted results) is called data dredging or p-hacking. This experiment is then brought to the surface and the scientists conclude that their new drug has improved the recovery time from the virus. Suppose that out of 20 experiments, only 1 experiment that reports a statistically significant result. The mean of (A) and (B) are then compared to check whether there’s a statistically significant difference between them. (B) Several people who recovered without the drug are asked to participate in the experiment & are then given the drug (A) Several people who recovered without the drug are asked to participate in the experiment Basically, here’s how each experiment is performed. Let’s say the used confidence level is 95%. They perform a statistical hypothesis testing which starts from stating that there’s no difference between the recovery time for people without & with the drug (null hypothesis). “If you torture the data long enough, it will confess to anything” - Ronald Coase.ĭata dredging / p-hacking for the sake of a published study.Ī group of scientists are asked for investigating whether a new developed drug improves the recovery time from a virus.
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