We had a recent project where the team confidently reported a p-value of 0.049 as ‘significant’ at a conference — the laughter that erupted when I casually mentioned we had run only 8 subjects was priceless. Makes you wonder how often people misinterpret data when they get too enamored with the stats.
I once submitted a paper where I focused too much on a p-value of 0.05, forgetting to really consider effect size. It was a big eye-opener when my colleagues pointed out how much variability there was in the data. Now I always remind myself that context matters just as much as those numbers. @StatisticalGuru has some great insights on this.
It’s hilarious how those p-values can make you feel like a stats wizard until you realize your sample size is smaller than a coffee shop’s regulars. I remember once presenting data from a study with only 10 people and trying to justify significance like I was defending a PhD thesis to a room full of skeptics. Next time, I’ll definitely keep the sample size in mind before letting my p-value get too big for its britches.