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Exact(12)
However, to the extent that we mixed occupational titles with high and low true exposures within the job groups, the observed mean values of the job groups would erroneously seem similar (ultimately, we could have constructed our job groups in such a poor way that all group means were equal, meaning that we would be unable to detect exposure-response relationships).
The null hypothesis was that all means were equal.
For nursing home residents, the curves were parallel (p = 0.4) and the means were equal (p = 0.3) between transfusion groups.
Therefore, the null hypothesis that the means were equal was rejected if the value of P was < 0·05.
Likewise, for sheltered housing residents, the curves were parallel (p = 0.6) and the means were equal (p = 0.9) between transfusion groups.
The null hypothesis for the Student's T-test was that the means were equal and the null hypothesis was rejected if the p-value was < 0.05.
Similar(48)
The null hypothesis is that the two means are equal, and the alternative is that they are not.
And so what we report and what we end up looking at in our hypothesis testing is whether means are equal.
We show that for balanced designs, the familywise error rate of the latter is largest when all of the means are equal but one.
The hypothesis that the population means are equal is considered equivalent to the hypothesis that there is no difference in treatment effects.
The null hypothesis is that the group means are equal.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com