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Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test. Significance is usually denoted by a p -value , or probability value.
Statistical significance is a term used by researchers to state that it is unlikely their observations could have occurred under the null hypothesis of a statistical test. Significance is usually denoted by a p -value , or probability value.
Statistical significance plays a pivotal role in statistical hypothesis testing. It is used to determine whether the null hypothesis should be rejected or retained. The null hypothesis is the hypothesis that no effect exists in the phenomenon being studied. [36]
In research, statistical significance measures the probability of the null hypothesis being true compared to the acceptable level of uncertainty regarding the true answer. We can better understand statistical significance if we break apart a study design.
Statistical significance indicates that an effect you observe in a sample is unlikely to be the product of chance. For statistically significant results, you can conclude that an effect you observe in a sample also exists in the population.
When you perform a statistical test, a p-value helps you determine the significance of your results in relation to the null hypothesis. The null hypothesis (H0) states no relationship exists between the two variables being studied (one variable does not affect the other).
In statistics, the significance level defines the strength of evidence in probabilistic terms. Specifically, alpha represents the probability that tests will produce statistically significant results when the null hypothesis is correct. Rejecting a true null hypothesis is a type I error. And, the significance level equals the type I error rate.