## Type 1 error

### In statistics, a Type 1 error, also known as a false positive error, occurs when a null hypothesis is rejected even though it is actually true

In statistics, a Type 1 error, also known as a false positive error, occurs when a null hypothesis is rejected even though it is actually true. In hypothesis testing, we assume that the null hypothesis is true and then collect data to determine if there is enough evidence to reject it in favor of an alternative hypothesis.

A Type 1 error is committed when we incorrectly reject the null hypothesis, leading to the conclusion that there is a significant effect or relationship when in reality, there is none. This can be problematic because it can lead to false conclusions and incorrect decisions.

To understand this concept better, let’s consider an example. Suppose a pharmaceutical company is testing a new drug to see if it is effective in treating a certain disease. The null hypothesis in this case would be that the drug has no effect on the disease, and the alternative hypothesis would be that the drug is effective.

If the company commits a Type 1 error, it means that they reject the null hypothesis and conclude that the drug is effective, even though it is not. This can lead to the drug being approved and distributed, potentially causing harm to patients and wasting resources.

To minimize the likelihood of Type 1 errors, researchers use statistical significance tests and establish a significance level (often denoted as alpha). The significance level represents the threshold at which the null hypothesis can be rejected. By choosing a smaller significance level (e.g., 0.05), the researchers can decrease the chances of making a Type 1 error.

In summary, a Type 1 error occurs when we incorrectly reject the null hypothesis, believing that there is an effect or relationship when there is none. It is important to minimize Type 1 errors in order to draw accurate conclusions from statistical analyses.

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