false positive

Terms from Statistics for HCI: Making Sense of Quantitative Data

This is where you wrongly believe something to be true. In traditional significance testing the choice of the significance level is precisely the probability of a false positive. For example, a p<5% means that there is a 1 in 20 chance of getting a false positive result. Note, though, that this only applies to the raw results; if some results are not published (the file drawer effect) the false positive result of published results may be higher. A false positive conclusion is known in statistics as a Type I error. Contrast with a false negative.

Used on pages 62, 105

Also known as false positive result