Statistical Errors -
In statistics, Type I and Type II errors are associated with hypothesis testing:
Type I Error (False Positive):
Definition: This occurs when you reject a null hypothesis that is actually true.
Interpretation: It's a mistake of concluding that there is an effect or difference when there isn't one.
Type II Error (False Negative):
Definition: This happens when you fail to reject a null hypothesis that is false.
Interpretation: It's a mistake of not detecting a real effect or difference that exists.
In essence, Type I error is about finding an effect that isn't there, while Type II error is about failing to detect an effect that is present. The balance between these errors is controlled by the significance level (α) and power of the statistical test. Adjusting these factors can impact the likelihood of making each type of error.
What is p-value
The p-value, or probability value, is a measure used in statistical hypothesis testing to assess the evidence against a null hypothesis. In hypothesis testing, the null hypothesis typically states that there is no effect or no difference between groups, and the alternative hypothesis posits the presence of an effect or a difference.
The p-value quantifies the probability of observing a test statistic (or more extreme) under the assumption that the null hypothesis is true. A low p-value suggests that the observed data is unlikely if the null hypothesis is true, leading to the rejection of the null hypothesis in favor of the alternative hypothesis.
Commonly, a significance level (alpha), often set at 0.05, is chosen. If the p-value is less than or equal to alpha, the result is considered statistically significant, and the null hypothesis is rejected. If the p-value is greater than alpha, there is not enough evidence to reject the null hypothesis.
It's important to note that a p-value does not provide the probability of the null hypothesis being true or false; rather, it indicates the strength of evidence against the null hypothesis based on the observed data.
