Type I and Type II Errors

In statistical hypothesis testing, Type I and Type II errors represent critical concepts that elucidate the potential inaccuracies in decision-making. A Type I error occurs when a true null hypothesis is mistakenly rejected, indicating a false positive. Conversely, a Type II error transpires when a false null hypothesis is not rejected, leading to a false negative. These errors hold paramount significance in various fields, influencing the reliability of conclusions drawn from statistical analyses and emphasizing the delicate balance between sensitivity and specificity in hypothesis testing. Understanding and mitigating these errors are crucial for ensuring the validity and robustness of statistical inferences.