Here are the essential concepts you must grasp in order to answer the question correctly.
P-value
The P-value is a statistical measure that helps determine the significance of results in hypothesis testing. It represents the probability of obtaining a test statistic as extreme as, or more extreme than, the observed value under the null hypothesis. A smaller P-value indicates stronger evidence against the null hypothesis, leading to a decision to reject it if the P-value is less than the significance level (α).
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Hypothesis Testing
Hypothesis testing is a statistical method used to make inferences about population parameters based on sample data. It involves formulating two competing hypotheses: the null hypothesis (H0), which represents no effect or no difference, and the alternative hypothesis (H1), which represents the effect or difference. The goal is to determine whether there is enough evidence to reject H0 in favor of H1 based on the calculated test statistic and corresponding P-value.
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Significance Level (α)
The significance level, denoted as α, is a threshold set by the researcher to determine when to reject the null hypothesis. Commonly set at 0.05, it represents a 5% risk of concluding that a difference exists when there is none (Type I error). If the P-value is less than or equal to α, the null hypothesis is rejected, indicating that the observed data is statistically significant at that level.
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