Error in Figure 3
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When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
The decision is not to reject null hypothesis when it is true (correct decision).
The decision is to reject the null hypothesis when it is true (incorrect decision known as a Type I error).
The decision is not to reject the null hypothesis when, in fact, it is false (incorrect decision known as a Type II error).
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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...