Choose the correct answer from the options given below:

​Given Statements:
Assertion (A): A hypothesis is accepted if the p-value is < 0.01.
Reason (R): Estimating of p-value accounts for correcting chance factor.

Correct option is B

Understanding Assertion (A) - "A hypothesis is accepted if the p-value is < 0.01":
-The p-value is a measure used in statistical hypothesis testing to determine the strength of evidence against the null hypothesis.
-The most commonly used significance levels (α) are 0.05 (5%) and 0.01 (1%).
-A p-value < 0.01 means that there is less than a 1% probability that the observed results happened due to chance.
-This suggests strong evidence against the null hypothesis, leading to its rejection.

Understanding Reason (R) - "Estimating p-value accounts for correcting chance factor":
-The p-value indicates the probability of obtaining the observed results due to random variation if the null hypothesis is true.
-However, it does not itself correct for chance factors; instead, statistical adjustments like Bonferroni correction or False Discovery Rate (FDR) correction are used for multiple comparisons.
-Thus, (R) is true but does NOT directly explain (A).

Since both statements are true, but (R) does not directly explain (A), option 2 is correct.

Information Booster:
-Hypothesis Testing: A statistical method used to determine if there is enough evidence to reject a null hypothesis.
-P-value: The probability of obtaining observed results if the null hypothesis is true. Lower p-values indicate stronger evidence against the null hypothesis.
-Common Significance Levels:
---p < 0.05 → Statistically significant (moderate confidence)
---p < 0.01 → Strong statistical significance (high confidence)
---p < 0.001 → Very strong statistical significance (extremely high confidence)
-Type I and Type II Errors:
---Type I error (False Positive): Incorrectly rejecting a true null hypothesis.
---Type II error (False Negative): Failing to reject a false null hypothesis.
-Multiple Comparisons Issue: When testing multiple hypotheses, the chance of obtaining false positives increases. Methods like the Bonferroni correction help control this.
-Effect Size: P-values alone do not indicate practical significance; effect size measures the magnitude of a result.