Sentence examples for many null hypotheses from inspiring English sources

With so many null hypotheses being tested simultaneously, the conventional single hypothesis testing criterion (p-value <img src="http://journals.plos.org/plosone/article/asset?id=info?doi/10.1371/journal.pone.0018874.e001.PNG" class= inline-graphic"/> 0.05) to reject a null hypothesis and declare significance in the result is no longer adequate, because too many false positives become inevitable.

The FDR was introduced with the aim to improve statistical efficiency (as compared with e.g. Bonferroni correction): reject as many null hypotheses as possible, while controlling a reasonable error rate.

Enzyme activity differs from the other two measurement types in that its parameters can have many plausible null hypotheses: the k catj can be equal to zero or to each other within groups defined in various ways.

In the framework of null hypothesis testing, this will lead to too many rejections of the null hypotheses when testing the population-wide mean slope against some specific value (slope main effect) or the slopes of 2 populations against each other (slope-by-treatment interactions).

Unlike the null hypotheses in many existing programs, the NEXT-peak model does not assume a globally uniform background intensity.

This might be less of a problem in studies on pesticides because these chemicals are designed to kill biota; thus in many cases, the null hypothesis might be an effect rather than the absence of one.

If many hypotheses are tested, inevitably some null hypotheses will be rejected just by chance (e.g., 5%% of null hypotheses if the chosen level of significance for individual hypotheses is p < 0.05).

In following the scientific process, it is often more difficult to prove something than to disprove it, so many scientists make incremental progress by disproving a series of smaller null hypotheses.

EDA emphasizes generating hypotheses by looking at lots of data in many different ways, as opposed to more "traditional" statistical approaches for testing null hypotheses.

If all null hypotheses are true, on average you can expect 500 false positives, far too many false leads in practice.

Three null hypotheses were tested: 1.