A Type I Error occurs when a true null hypothesis is incorrectly rejected, often referred to as a 'false positive'. It is controlled by the significance level (alpha), which represents the probability of making this error in hypothesis testing.

Type I Error

true null hypothesis

A false positive occurs when a test incorrectly indicates the presence of a condition, such as a disease or defect, when it is not actually present. This can lead to unnecessary stress, additional testing, and potential treatment for a condition that does not exist.

false positive

The significance level, often denoted by alpha, is the threshold for determining whether a statistical hypothesis test result is statistically significant. It represents the probability of rejecting the null hypothesis when it is actually true, and is commonly set at 0.05 or 5%, indicating a 5% risk of concluding that a difference exists when there is no actual difference.

significance level

In finance, 'alpha' refers to the excess return on an investment relative to the return of a benchmark index, often used as a measure of an investment's active return. In the context of social dynamics, 'alpha' denotes a dominant individual who leads or influences others within a group.

alpha

probability of making this error

Hypothesis testing is a statistical method used to make decisions about the properties of a population based on a sample. It involves formulating a null hypothesis and an alternative hypothesis, then using sample data to determine which hypothesis is more likely to be true.

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