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Even if a study meets the benchmark requirements for and , and is free of bias, there is still a 36% probability that a paper reporting a positive result will be incorrect; if the base probability of a true result is lower, then this will push the PPV lower too. Furthermore, there is strong evidence that the average statistical power of a study ...
The former report is adequate, the latter gives a more detailed explanation of the data and the reason why the suitcase is being checked. Not rejecting the null hypothesis does not mean the null hypothesis is "accepted" per se (though Neyman and Pearson used that word in their original writings; see the Interpretation section).
The results are then reported by a newspaper as indicating that green jellybeans are linked to acne at a 95% confidence level—as if green were the only color tested. In fact, if 20 independent tests are conducted at the 0.05 significance level and all null hypotheses are true, there is a 64.2% chance of obtaining at least one false positive ...
Investigators may also assume they made a mistake, find that the null result fails to support a known finding, lose interest in the topic, or anticipate that others will be uninterested in the null results. [8] There are several scientific journals dedicated to the publication of negative or null results, including the following:
This is the most popular null hypothesis; It is so popular that many statements about significant testing assume such null hypotheses. Rejection of the null hypothesis is not necessarily the real goal of a significance tester. An adequate statistical model may be associated with a failure to reject the null; the model is adjusted until the null ...
Positive-results bias, a type of publication bias, occurs when authors are more likely to submit, or editors are more likely to accept, positive results than negative or inconclusive results. [15] Outcome reporting bias occurs when multiple outcomes are measured and analyzed, but the reporting of these outcomes is dependent on the strength and ...
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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 ...