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1. Test of Significance & its uses
in statistics

2. What is Test of Significance?
• A test of significance is a statistical procedure used to
determine whether the observed data provide enough
evidence to reject a stated hypothesis (usually the null
hypothesis). It helps assess whether an observed effect,
difference, or relationship is real or simply due to
random chance.
• The P value is the probability of rejecting or failing to
reject the null hypothesis (H0).

3. • Level of significance:
The significance level is the maximum probability of
rejecting H0 when it is true, and it is usually determined
in advance before testing the hypothesis.
• Confidence Interval:
A confidence interval provides a range of values
within which the true population parameter (such as
mean or proportion) is likely to lie.

4. Common Tests in Medical Research
• t-test: compares means of two groups.
• Chi-square test: association between two variables.
• ANOVA: compares more than two means.
• Correlation: relationship between variables.

6. Assumptions of Parametric Tests
• Parametric tests (e.g., t-test, ANOVA, Pearson correlation) require certain
conditions:
• Normality
Data should be approximately normally distributed (in each group/sample).
• Homogeneity of Variance
Groups being compared should have equal variances (e.g., for t-test,
ANOVA).
• Independence
Observations should be independent of each other.
• Measurement Scale
Data must be measured on an interval or ratio scale.
• Random Sampling
Samples should be drawn randomly from the population.

7. Assumptions of Non-Parametric Tests
• Non-parametric tests (e.g., Mann–Whitney U, Kruskal–Wallis, Chi-
square) require fewer or weaker assumptions:
• No Need for Normality
Can be applied even when data are not normally distributed.
• Ordinal or Nominal Data Allowed
Works with ordinal, nominal, or non-normally distributed interval data.
• Independence
Observations should be independent (except in paired tests like Wilcoxon
signed-rank).
• Shape of Distribution Not Assumed
No assumption about mean or variance of population.
• Random Sampling
Samples should be randomly selected.

8. Uses of Significance Tests
• To compare the means of groups.
• To determine treatment effectiveness
• To verify research hypotheses.
• To support medical decision-making.

9. Summary
• Statistical tests help analyze data.
• Test of significance checks if results are due to
chance.