Hypothesis Testing

Hypothesis Testing Basics

• Hypothesis testing is a systematic procedure for making statistical decisions using experimental data.
• Hypothesis testing is based on an initial null hypothesis (H0) and an alternative hypothesis (H1).
• The null hypothesis is the hypothesis you want to disprove. It usually suggests no effect or no difference in population.
• The alternative hypothesis is what you might believe to be true or hope to prove true.

Steps of Hypothesis Testing

• Firstly, state the null and alternative hypotheses. This is based on the research question or problem being addressed.
• Next, choose an appropriate test statistic based on the nature of data and the samples.
• Then, use the test statistic to calculate the P-value. The P-value is the probability of observing a result at least as extreme, provided that the null hypothesis is true.
• Finally, make a decision based on the P-value and a preset significance level (α), which is the probability of rejecting the null hypothesis when it is, in fact, true. Commonly used significance levels are 0.05, 0.01, and 0.001.

Types of Errors in Hypothesis Testing

• Type I error occurs when we reject the null hypothesis, but it is actually true. It’s like a false positive. The probability of making a Type I error is denoted by α.
• Type II error occurs when we fail to reject the null hypothesis, but it is actually false. It’s like a false negative. The probability of making a Type II error is denoted by β.

Importance and Relevance

• Hypothesis testing is commonly used in the field of statistics and data analysis to accept or reject statistical claims.
• These tests are of immense importance in making decisions based on data analysis and can be applied to a variety of situations, like scientific research, market research, quality control etc.
• Hypothesis testing is an important step in the scientific method, where it helps researchers understand the strength of the effect of their interventions.