What are the types of hypothesis testing?

Hypothesis Testing

There are several main types of hypothesis testing, each suited to analyzing different kinds of data and research questions. Here’s a breakdown of some common types:

Parametric Tests:

• These tests rely on the assumption that the data follows a specific probability distribution (like normal distribution).
  • Z-test: Used to compare the mean of one sample to a known value or compare the means of two independent groups when the sample sizes are small, and the population standard deviations are known to be equal.
  • T-test: Similar to a z-test but used when the population standard deviation is unknown, or the sample sizes are small (often used with estimates from the data itself). There are different variations of t-tests for different scenarios (e.g., one-tailed vs. two-tailed, paired vs. independent samples).
  • ANOVA (Analysis of Variance): Compares the means of more than two groups and helps determine if there’s a significant difference between them.

Non-Parametric Tests:

• These tests make fewer assumptions about the underlying data distribution and can be used with non-normal data or ordinal data (ranked data).
• Examples:
  • Chi-Square Test: Used to assess the relationship between two categorical variables or test if observed frequencies differ significantly from expected frequencies in a single categorical variable.
  • Mann-Whitney U Test: Compares the medians of two independent groups and is a non-parametric alternative to the two-tailed t-test.
  • Wilcoxon Signed-Rank Test: Compares the medians of two related samples (paired data) and is a non-parametric alternative to the paired t-test.

Choosing the Right Test:

The type of hypothesis test you choose depends on your specific research question and data characteristics:

• Data Type: Consider if your data is continuous (e.g., height, weight) or categorical (e.g., hair color, job category).
• Sample Size: Some tests, like z-tests, have specific assumptions about sample size.
• Number of Groups: Are you comparing two groups, multiple groups, or a single sample to a known value?
• Normality: Is your data normally distributed, or is it skewed or non-normal?

Statistical Tests

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Also Read:

• Wilcoxon Signed-Rank Test Explained
• Mann-Whitney U Test Explained
• Chi-Square Test Explained
• ANOVA Analysis of Variance Explained
• T-Test Explained
• Z-Test Explained