Sentence examples for a correct null hypothesis from inspiring English sources

The phrase "a correct null hypothesis" is correct and usable in written English.
It can be used in the context of statistical analysis or hypothesis testing to refer to a null hypothesis that is formulated accurately according to the principles of statistics.
Example: "In our study, we established a correct null hypothesis to ensure the validity of our results."
Alternatives: "an accurate null hypothesis" or "a valid null hypothesis".

Exact(1)

That may be surprising, but nonetheless it is possible to use a correct null hypothesis rather than TFnuGTR.

Similar(59)

As a consequence of the nature of the statistical test used here we reject the correct null hypothesis in 5% of the cases by chance and we thus effectively expect to obtain F max = 0.95 even if the dataset is completely featureless.

A type I error is a false positive, rejecting a null hypothesis that is correct.

For "low-confidence" calls, made with respect to a canonical sequence taken as a "null hypothesis", those that match that sequence are counted as correct, and those that do not are counted as "N".

Research can't prove a negative (we can't prove a null hypothesis; we reject a null hypothesis); research can technically only find no relationship.

We discuss and articulate design considerations for adaptive enrichment among a dual-composite null hypothesis, a flexible dual-independent null hypothesis and a rigorous dual-independent null hypothesis.

Second, because of the excessive number of outcomes investigated, the HAPO investigators had to correct their P values for multiple testing, which may have increased the risk of a type I error (rejecting a null hypothesis when it is true).

However because the Bootstrap introduces a bias in the error estimates that is difficult to correct, the above procedure samples without replacement and tests only for a restricted null hypothesis (i.e., "bad" data split).

For example, a variety of studies do not correct for multiple comparisons, leading to type 1 errors (incorrect rejection of a true null hypothesis), thus presenting false positive results.

Failing to correct an error term appropriately increases the probability of a type II error – failure to reject a null hypothesis that should be rejected.

In science terms we call this a null hypothesis, or a statement of no effect.

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