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The significance level, often denoted as alpha (α), is a threshold used in hypothesis testing to determine whether to reject the null hypothesis. Commonly set at 0.05, it represents the probability of making a Type I error, which occurs when a true null hypothesis is incorrectly rejected. Understanding this concept is crucial for assessing whether a result, such as the number of matches, is statistically significant.

Statistical power is the probability that a test will correctly reject a false null hypothesis. It is influenced by the sample size, effect size, and significance level. A low number of matches, like 1, may lead to low power, making it difficult to detect a true effect if one exists. This concept helps in evaluating whether the observed number of matches is sufficient to draw meaningful conclusions.

Contextual interpretation involves analyzing data within the framework of the specific situation or experiment. In this case, determining whether 1 match is significantly low requires understanding the expected number of matches based on prior data or theoretical models. This concept emphasizes that statistical results should not be viewed in isolation but rather in relation to the broader context of the study.