Reason (R) : The alternate hypothesis is accepted only when null hypothesis is rejected.
Codes :
The assertion states that the hypothesis a researcher tests is the null hypothesis. In statistical hypothesis testing, researchers typically formulate a null hypothesis (denoted as $H_0$), which represents a statement of no effect or no difference. This is the specific hypothesis that is subjected to statistical analysis to determine if it can be rejected based on the collected data. Therefore, the assertion is true.
The reason states that the alternative hypothesis (denoted as $H_1$ or $H_a$) is accepted only when the null hypothesis ($H_0$) is rejected. This aligns with the standard procedure of hypothesis testing. If the evidence from the sample data is strong enough to reject $H_0$ at a predetermined significance level, the conclusion drawn is typically in favor of the alternative hypothesis $H_1$. Therefore, the reason is true.
While both the assertion (A) and the reason (R) are true statements regarding hypothesis testing:
Reason (R) does not explain *why* the null hypothesis is the one formally tested (Assertion A). It explains what happens when the test result leads to rejecting the null hypothesis. The reason for testing the null hypothesis relates to the structure of statistical inference and the principle of falsification, not directly to the condition for accepting the alternative hypothesis. Thus, (R) is true, but it is not the correct explanation for (A).
Based on the analysis, both Assertion (A) and Reason (R) are true, but Reason (R) does not correctly explain Assertion (A).
For the ANOVA table
| Source of variations | Sum of squares | Degree of freedom |
| Between treatment | 75 | 3 |
| Error | 48 | 16 |
| Total | 123 | 19 |
the F - statistics is
In a 3 races, 2 genders and 5 in each treatment group for two-way ANOVA, the degree of freedom for source of variation due to interaction, error and total respective are
The Pearson's correlation coefficient between following observation
| X: | 1 | 2 | 3 | 4 |
| Y: | 3 | 4 | 2 | 1 |
is -0.8. If each observation of X is halved and of Y is doubled, then Pearson's correlation coefficient equals to
For the ANOVA table
| Source of variations | Sum of squares | Degrees of freedom |
| Between treatment | 45 | 3 |
| Error | 32 | 16 |
| Total | 99 | 19 |
the F - statistics is:
For the ANOVA, which of the following options is INCORRECT?