TEST OF SIGNIFICANCE

TEST OF SIGNIFICANCE

🔹 DEFINITION
A test of significance is a statistical method used to determine whether the observed differences between groups or relationships between variables are real or occurred by chance.

PURPOSE OF TEST OF SIGNIFICANCE

• To verify the validity of a hypothesis (usually the null hypothesis)

• To help in decision making in research

• To determine whether the results of a study are statistically significant

STEPS INVOLVED

1. Formulation of Null and Alternative Hypotheses

   • Null Hypothesis (H₀): No effect or difference

   • Alternative Hypothesis (H₁): There is an effect or difference

2. Selection of Significance Level (α)

   • Common values: 0.05 or 0.01

   • It represents the probability of rejecting the null hypothesis when it is true

3. Selection of Test Statistic

   • Based on type of data and distribution (e.g., t-test, z-test, chi-square test)

4. Calculation of Test Statistic

   • Using appropriate formula based on the test applied

5. Comparison with Critical Value or p-value

   • If p-value < α → Reject H₀ (statistically significant)

   • If p-value ≥ α → Do not reject H₀ (not significant)

COMMON TESTS OF SIGNIFICANCE

• t-Test: Compares means of two groups

• z-Test: Used when sample size is large (n > 30)

• Chi-Square Test: Used for categorical data to test association

• ANOVA: Compares means of more than two groups

INTERPRETATION

• Statistically Significant Result: The observed effect is unlikely due to chance

• Not Significant: The observed effect could be due to random variation

IMPORTANCE IN RESEARCH

• Helps validate study outcomes

• Provides a scientific basis for acceptance or rejection of hypotheses

• Enhances credibility of conclusions

• Supports evidence-based decision making