Hypothesis Testing Statistics | Hypothesis Testing | Type 1 and Type 2 Errors | Significance Level | p-value

In this lecture, we will discuss hypothesis testing for statistics, Type 1 and Type 2 errors, Significance Level, and p-value in Research Methodology.

Hypothesis Testing and Errors

Hypothesis Testing in statistics, type 1 and Type 2 errors, Significance level, p-value

Hypothesis testing is a statistical method used to make inferences about population parameters based on sample data. In this process, you typically have a null hypothesis (H0) and an alternative hypothesis (H1).

Type 1 and Type 2 Errors – Hypothesis Testing Statistics

• Type 1 Error (False Positive): This occurs when you reject the null hypothesis when it is actually true. It represents a situation where you conclude there is an effect or difference when there isn’t. It’s often denoted as alpha (α), which is the significance level or the probability of making a Type 1 error.
• Type 2 Error (False Negative): This occurs when you fail to reject the null hypothesis when it is actually false. It means you miss a real effect or difference. It’s often denoted as beta (β).

Significance Level

• Significance Level (α): This is the probability of making a Type 1 error, which is the chance of wrongly rejecting the null hypothesis. Commonly used significance levels are 0.05 (5%) and 0.01 (1%), but it can be set to other values depending on the study.

P-Value

• p-value: The p-value is a measure of the evidence against the null hypothesis. It represents the probability of observing sample data as extreme as what you collected, assuming the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis. If the p-value is less than the chosen significance level (α), you typically reject the null hypothesis.

In hypothesis testing, you aim to strike a balance between minimizing Type 1 and Type 2 errors, usually by choosing an appropriate significance level and interpreting the p-value.