- Thread starter Drilon
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It is statistically significant (p < 0.001). That means, based on the sample data,

you assume that in the population from which the sample was drawn the

correlation is different from (larger than) r=0.00000

With kind regards

Karabiner

you assume that in the population from which the sample was drawn the

correlation is different from (larger than) r=0.00000

With kind regards

Karabiner

Thank you very much!

So I can say that correlation exists, higher the X, higher Y gets. But, what about .28? is it to low?

This is a sample coefficient. It is not the population coefficient. The sample coefficient is larger

or smaller than the true population coefficient, due to sampling error. But in the present case,

the sampling error is small, because the sample size is large. So maybe the true coefficient is

near 0.28. Whether this large or small, depends on the field of study. In psychology, sociology,

medicine, biology r=0.3 is often considered medium sized. But if you had reason to assume

a coefficient of size r=0.8 or so, then 0.28 would obviously be small.

With kind regards

Karabiner

or smaller than the true population coefficient, due to sampling error. But in the present case,

the sampling error is small, because the sample size is large. So maybe the true coefficient is

near 0.28. Whether this large or small, depends on the field of study. In psychology, sociology,

medicine, biology r=0.3 is often considered medium sized. But if you had reason to assume

a coefficient of size r=0.8 or so, then 0.28 would obviously be small.

With kind regards

Karabiner

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(i.e. r > 0.7!) are worth considering, would plainly be silly. It contradicts most of the empirical work done in the

social and life sciences. The examples in that document make that claim even more dubious. For example, they

discuss an R² of 0.7 (i.e. r = 0.83!) when explaining happiness; given that happiness measures have a reliability of

0.8 at best, that would mean that they want to explain nearly the complete non-error variance by k=1 other

variable. Such goals are more than...heroic.

With kind regards

Karabiner