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Test for significance is not everything, it is important to know how does it influence.

In your case you may get a result like: "The fact that a person

Test for significance is not everything, it is important to know how does it influence.

What test would I use to compare a binary predictor to a binary DV, though? It wouldn't still be logistic regression, would it?

Test for significance is not everything, it is important to know how does it influence.

In your case you may get a result like: "The fact that a person

When you say that significance is not everything, what does that mean for my results? Using your example, if the results show that odds increase by **23%**, but p > 0.05, does that mean that **knowing a scientist** does not have an impact on **positive embodiment**?

If we ignore for a moment the multiple tests problem, and under the assumption that you took the proper sample size:

P value=0.23 says only that there is a probability of 0.23 that there is no effect.

or there may be a meaningless effect, per your definition, that is smaller than the required effect you used to calculate the sample size.

But if P value=0.00001, you know there is an effect, but it doesn't say anything about the effect size.

For example, you may find that medication improves the disease healing process with P value=0.00001, but the effect is very small, like healing time will be improved from 90 days to 89.8 days. you probably won't use this medication ...

Hi Noetsi ,

The best way I found to understand logistic regression: how a change in one unit of the predictor will increase/decrease the odds of an event (1).

The best way I found to understand logistic regression: how a change in one unit of the predictor will increase/decrease the odds of an event (1).

This is what I am talking about.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2938757/

Relative risk may be even better, but most software does not calculate that I think (I can't speak to R it may).

Is _cons interval or a categorical predictor (and how is it coded. For example 1/0 if it has two levels).

I did not see an intercept. Did you suppress it? If you did it changes the interpretation (which is why I don't suppress it).

Is _cons interval or a categorical predictor (and how is it coded. For example 1/0 if it has two levels).

I did not see an intercept. Did you suppress it? If you did it changes the interpretation (which is why I don't suppress it).

I think that is an odds ratio

This is what I am talking about.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2938757/

The relative risk maybe even better, but most software does not calculate that I think (I can't speak to R it may).

This is what I am talking about.

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2938757/

The relative risk maybe even better, but most software does not calculate that I think (I can't speak to R it may).

It seems that R has the "relative risk".

https://cran.r-project.org/web/packages/logisticRR/vignettes/logisticRR.html