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Also confidence coefficient. A number indicating the probability that the confidence interval (range) captures the true population mean. For example, a confidence interval with a 95% confidence level has a 95% chance of capturing the population mean. Technically, this means that, if the experiment were repeated many times, 95% of the CIs computed at this level would contain the true population ...
Researchers focusing solely on whether their results are statistically significant might report findings that are not substantive [46] and not replicable. [47] [48] There is also a difference between statistical significance and practical significance. A study that is found to be statistically significant may not necessarily be practically ...
One technique used in factorial designs is to minimize replication (possibly no replication with support of analytical trickery) and to combine groups when effects are found to be statistically (or practically) insignificant. An experiment with many insignificant factors may collapse into one with a few factors supported by many replications. [44]
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In null-hypothesis significance testing, the p-value [note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct.
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The F-test in ANOVA is an example of an omnibus test, which tests the overall significance of the model. A significant F test means that among the tested means, at least two of the means are significantly different, but this result doesn't specify exactly which means are different one from the other.