What’s Significant? Unpacking the P-Value

What’s this, two reblogs in a row? It must seem like I’m running out of ideas. In actuality, this post by Anna Marchand of Plebeian Science is just really good.

Anna does a great job of describing the concept of p-value, or the probability that an observed pattern happened by random chance. P-values are a vital part of determining whether the results of a scientific study are ‘significant,’ and as Anna pointed out, whether or not a scientist gets paid.

While I’m not entirely fond of Anna’s blaspheming against Douglas Adams, this post is definitely worth a read. It made me nostalgic for my undergraduate Statistics for Behavioral Sciences course, which was when I realized that I actually kind of like math.

Please follow the link below to read Anna’s excellent post about p-values!

Plebeian Science

Douglas Adams once famously wrote that the “answer to the ultimate question of life, the universe and everything is 42.”

I beg to differ. For many scientists, the answer is any number less than or equal to 0.05.

Why, you ask? The answer lies in p-values. If you’ve never worked heavily with statistics, you might think this is the lead up to a bathroom joke (“No, no, I mean, ‘P’ like the letter, not like THAT…”).  What is a p-value, then? I offer you three definitions:

My Flippant Definition: The p-value is an arbitrary number that determines if a scientist gets paid.

Internet Definition: “The p-value, or calculated probability, is the probability of finding the observed, or more extreme, results when the null hypothesis (H) of a study question is true – the definition of ‘extreme’ depends on how the hypothesis is being tested” (source

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