The document discusses statistical significance tests and the F test. The significant test determines whether data supports or rejects the null hypothesis. The F test compares the variance of two samples and follows an F distribution. It is used to test hypotheses about the equality of variances or means of multiple populations. Assumptions of the F test include random sampling from normal populations with equal but unknown standard deviations and independent samples.

1. Significant Test:
• The test which is done for testing the
research hypothesis against null
hypothesis.
• It play a key role in experiments.
• It allow researchers to determine
whether their data supports or rejects
the null hypothesis.
• They can accept their null hypothesis.

2. F Test
• F Test is basically a comparison between test
and accepted method.
• F stand for a Fisher A famous statistician
• By using standard deviation or variance.

3. F Test:
• Also known as analysis of variance
• The test is on base on test statistics which follow F
distribution is called F test
• In the F test ratio we always take the larger of the two
estimates in the numerator and smaller is the denominator
• The degree of freedom is n-1

4. F Test
• If experimental value is less
than that of tabulated value
then the result will be a
significant.
• If experimental value is
greater than that of
tabulated value then the
result will not be a
significant.

7. Uses of F test:
• It is used to test hypothesis of equality
of two population variances.
• It is used to test equality of mean of
three or more populations.
• It is used for testing the significances
of an observed sample multiple
question.

8. Assumptions:
• Samples are drawn at random.
• Samples are drawn from the normal
populations.
• Population standard deviation are treated as
equal but unknown.
• The samples must be independent