This is a key part of the NHST (Null Hypothesis Significance Testing) paradigm, see null hypothesis and inferential testing for more on that generally. Within that model the type I error is the risk you run of rejecting the null hypothesis if it is true for the population. That is to say the risk of declaring your finding “non-significant” even though, in the population from which you have your sample, actually the null hypothesis is not true and actually there is something systematic going on.
The risk of making such a type I error is something you (in principle) set before you start doing your study (see “pre hoc hypothesis” and “pre hoc hypothesis”). By a rather intriguing and highly debatable historical convention this is usually .05, i.e. that you are accepting a one in twenty risk of a “false positive”, of rejecting the null and accepting the alternative that something systematic of potential interest (in the terms of the test) is happening when in fact in the population there is no effect.
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Try also #
pre hoc hypothesis
post hoc hypothesis
Online resources #
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First created 20.viii.23.