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 researchers to consider rejecting it.
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Hypothesis Testing
Hypothesis testing is a statistical method used to make decisions about population parameters based on sample data. It involves formulating a null hypothesis (H0) and an alternative hypothesis (H1), then using sample data to calculate a test statistic. The outcome determines whether there is enough evidence to reject H0 in favor of H1 at a specified significance level (α).
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Significance Level (α)
The significance level, denoted as α, is the threshold for determining whether to reject the null hypothesis in hypothesis testing. It represents the probability of making a Type I error, which occurs when the null hypothesis is incorrectly rejected. Common values for α are 0.05, 0.01, and 0.10, with lower values indicating a stricter criterion for significance.
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