Statistics for Absolute Beginners: A Plain English Introduction by O. Theobald

Author:O. Theobald [Theobald, O.]
Language: eng
Format: azw3
Published: 2017-09-27T16:00:00+00:00

Figure 15: Example of Type I and Type II Errors in hypothesis testing

The first mistake is a Type I Error, which is the rejection of a null hypothesis (H0 ) that was true and should not have been rejected. This means that although the data appears to support that a relationship is responsible, the covariance (a measurement of how related the variance is between two variables) of the variables is occurring entirely by chance. Again, this does not prove that a relationship doesn’t exist, merely that it’s not the most likely cause. This is commonly referred to as a false-positive .

Conversely, a Type II Error is accepting a null hypothesis that should’ve been rejected because the covariance of variables was probably not due to chance. This is also known as a false-negative .

Let's use a pregnancy test as an example. In this scenario, the null hypothesis (H0 ) holds that the woman is not pregnant. The null hypothesis is therefore rejected if the woman is pregnant (H0 is false) and accepted if the woman is indeed not pregnant (H0 is true).

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