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A frequent problem in personal science is that you tried an intervention and want to see if it worked. But you are unsure whether any differences you observed are maybe just by chance. So how can you see how likely is it that the results were chance?

A frequent problem in personal science is that you tried an intervention and want to see if it worked. But you are unsure whether any differences you observed are maybe just by chance. So how can you see how likely is it that the results were chance? One of the simplest tests is a '''T-Test''', sometimes called a “Student T Test”<ref>https://en.wikipedia.org/wiki/Student%27s_t-test</ref>, which provides you with a ''p-value'' after doing the t-test.

== Solution ==

== Background ==

 

Generally, statisticians use the concept of ''p-value''s to discuss how often you would expect to observe the effect you did observe just by chance. A a simplified example, a p-value of 0.05 means that you would randomly observe this effect in 5 out of 100 retries of your intervention just by pure chance. While this crude measure doesn’t describe all the ways something might happen due to chance, generally the lower the p-value, the better.   

One of the simplest tests is a '''T-Test''', sometimes called a “Student T Test”<ref>https://en.wikipedia.org/wiki/Student%27s_t-test</ref>, which provides you with a ''p-value'' after doing the t-test. Generally, statisticians use the concept of ''p-value''s to discuss how often you would expect to observe the effect you did observe just by chance. A a simplified example, a p-value of 0.05 means that you would randomly observe this effect in 5 out of 100 retries of your intervention just by pure chance. While this crude measure doesn’t describe all the ways something might happen due to chance, generally the lower the p-value, the better.   

Professional scientists, especially those who understand statistics, are skeptical of claiming a result based purely on p-values, but for Personal Science purposes, it’s a good start. There is no “''correct”'' p-value cutoff that determines whether an effect is not due to chance alone, but traditionally people assume that any p-value that is smaller than 0.05 deserves a closer look and indicates that is probably a '''real''<nowiki/>' effect.

Professional scientists, especially those who understand statistics, are skeptical of claiming a result based purely on p-values, but for Personal Science purposes, it’s a good start. There is no “''correct”'' p-value cutoff that determines whether an effect is not due to chance alone, but traditionally people assume that any p-value that is smaller than 0.05 deserves a closer look and indicates that is probably a '''real''<nowiki/>' effect.