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17/07/2025
Types of Hypothesis Testing
Hypothesis testing is a fundamental aspect of inferential statistics used to make decisions or inferences about population parameters based on sample data. In hypothesis testing, researchers assess whether the observed data provides sufficient evidence to reject a null hypothesis in favor of an alternative hypothesis. The main types of hypothesis tests are categorized based on the nature of the comparison and the statistical technique employed.
1. One-Tailed Test (Directional Test)
A one-tailed test is used when the researcher has a specific direction of interest in the hypothesis. It tests for the possibility of the relationship in one direction only.
Example: Testing whether the average score of students is greater than 50.
Form:
Null Hypothesis (H₀): μ ≤ 50
Alternative Hypothesis (H₁): μ > 50
There are two types of one-tailed tests:
Right-tailed test (H₁: μ > value)
Left-tailed test (H₁: μ < value)
2. Two-Tailed Test (Non-directional Test)
A two-tailed test is used when there is no specific direction in the hypothesis. It checks whether the sample mean is significantly different (either higher or lower) from the population mean.
Example: Testing whether the average weight of a product is not equal to 500g.
Form:
Null Hypothesis (H₀): μ = 500g
Alternative Hypothesis (H₁): μ ≠ 500g
This is common when researchers are interested in any significant difference regardless of direction.
3. Z-Test
Used when:
The population variance is known.
The sample size is large (n ≥ 30).
It compares the sample mean to a known population mean.
4. T-Test
Used when:
The population variance is unknown.
The sample size is small (n < 30).
Types of t-tests include:
One-sample t-test
Independent two-sample t-test
Paired sample t-test
5. Chi-Square (χ²) Test
Used for testing relationships between categorical variables. It assesses how expectations compare to actual observed data.
Types:
Chi-square test for independence
Chi-square test for goodness of fit
6. ANOVA (Analysis of Variance)
Used to compare means among three or more groups. It tests whether there is a significant difference among group means.
One-way ANOVA: One independent variable
Two-way ANOVA: Two independent variables
17/07/2025
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