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Type I error, also known as a "false positive" or "alpha error," occurs in statistical hypothesis testing when a true null hypothesis is incorrectly rejected. In other words, it happens when a test incorrectly concludes that there is a significant effect or difference when, in reality, there is no such effect or difference.

To understand Type I error, it's essential to grasp the basic concepts of hypothesis testing:

**Null Hypothesis**: A statement that there is no significant difference, effect, or relationship in the population. It represents the status quo or the default assumption.**Alternative Hypothesis**: A statement that contradicts the null hypothesis, suggesting the presence of a significant difference, effect, or relationship.**Significance Level (α)**: The chosen probability threshold for rejecting the null hypothesis. Commonly used values include 0.05, 0.01, or 0.10.

In a hypothesis test, the goal is to determine whether there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis. A Type I error occurs when the null hypothesis is rejected even though it is true.

The probability of committing a Type I error is equal to the chosen significance level (α). For example, if the significance level is set at 0.05, the probability of making a Type I error is 5%.

P(Type I error) = P (Reject Null Hypothesis | Null Hypothesis is True)

Common examples of Type I errors include:

**Medical Testing**: Concluding that a patient has a disease when, in fact, they do not (false positive).**Criminal Justice**: Wrongfully convicting an innocent person (rejecting the null hypothesis of innocence).**Quality Control**: Rejecting the null hypothesis that a manufacturing process is working properly when it is actually within acceptable limits.

Researchers and analysts aim to minimize the risk of Type I errors by selecting an appropriate significance level, conducting power analyses, and carefully interpreting statistical results. The choice of significance level involves a trade-off between the risk of Type I and Type II errors. A lower significance level reduces the risk of Type I errors but may increase the risk of Type II errors (failing to reject a false null hypothesis).