Statistical testing is used for exploring hypotheses about the possible existence of effects (differences, statistical relations). One chooses a statistical test mainly on the basis of which type of variable or which distributional characteristic of a variable is to be compared and related. Each statistical test has its own type of test statistic that captures the amount of effect/difference observed in the sample data. The problem with observed effects in samples is that they are influenced by sampling variation (chance) and may not accurately represent real population effects. P-values are therefore attached to the observed values of a test statistic in an attempt to acquire better insight into whether an observed effect is real. P-values are the probability of finding the observed value of the test statistic, or a value more extreme than it, when the null hypothesis (that there is absence of an effect or difference) is in fact true. As such, P-values are sometimes but not always a good basis for accepting or rejecting a null hypothesis. After discussing the uses of statistical testing in epidemiology and different types of hypotheses to test, we discuss the interpretations of P-values and conclude with a brief overview of commonly used statistical tests.
Null Hypothesis Alternative Hypothesis Prior Probability Null Hypothesis Test Null Case
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