Menon - Mitochondrial Modifying Nutrients in Treating Chronic Fatigue Syndrome: A 16-week Open-Label Pilot Study

The way stats testing works is that you try to reject the null hypothesis (that two groups are the same) and in doing do try to show a difference between the groups. In doing this the sample size makes a difference. This is why in doing a trial a power calculation is normally done to workout the chance given an effect that the null hypothesis will be rejected. It uses the sample size, predicted effect size and the significance test level (including multitest corrections). This is used to give a minimum estimated sample size for a trial to lead to significance given the predicted effect size. Its normally required for ethical approval.

So having more patients could lead to a significant result. However, they will have done a power calculation so it would suggest that the effect is smaller than predicted (if any).

The intuition would be something like if you draw more samples from a population and see an effect you get more certainty than if you draw a smaller number. But to me significance testing is not intuitive.

 

Useful explanation! Do you want to join the Stats team, @Adrian? ! @Graham and I, with some others are starting to write short explanations of some aspects of the stats that appears in scientific papers to put in the library.

 

I agree and if you need high numbers in a trial chances are the effect is small.