or look at the variance and only consider correlations greater than, say, 0.5

or use both: a p value of less than 5% and a correlation greater than 0.5?

p.s. I thought I'd posted this yesterday, but cannot see it.

- Thread starter Zarathrustra
- Start date
- Tags correlation regression

or look at the variance and only consider correlations greater than, say, 0.5

or use both: a p value of less than 5% and a correlation greater than 0.5?

p.s. I thought I'd posted this yesterday, but cannot see it.

The two characterizations are separate:

1] "The effect is*statistically significant*" means that the true parameter has a value different from the value in the null hypothesis. In the modeling setting, this typically implies that the corresponding predictor has a relationship with the dependent variable and should stay in the model.

2] "The effect is*substantial*" means that the size of the true parameter is big enough to describe a relationship affecting real life.

Whether "correlation above 0.5" is substantial enough depends on your situation, on the context of your problem (looks substantial to me). Whether "p-value below 0.05" indicates statistical significance depends on the preset significance level, dictated by your research standards and the research standards in your industry.

I cannot comment on whether the t-test is appropriate. Agree with hlsmith: you have to describe the research question(s) and data.

1] "The effect is

2] "The effect is

Whether "correlation above 0.5" is substantial enough depends on your situation, on the context of your problem (looks substantial to me). Whether "p-value below 0.05" indicates statistical significance depends on the preset significance level, dictated by your research standards and the research standards in your industry.

I cannot comment on whether the t-test is appropriate. Agree with hlsmith: you have to describe the research question(s) and data.

Last edited:

One way to think of test of statistical significance is required, but not sufficient. if you don't have statistical significance than you probably should ignore the slope. But just having it does not mean the slope (essentially the relationship between the variables controlling for others) matters.

I am relating many lifestyle independent variables against somewhat fewer (but still a lot) of independent biomarkers.

For some, such as blood pressure and body composition, I have nearly 3,000 daily events and so I can do multiple regression analysis (using Statistica) for many independent variables at once, but for others (done much more sporadically) I have a relatively few events (ranging from a dozen or so to nearly 100) for each independent variable against each dependent variable.

My concern was that a correlation needed to be higher for fewer events for it to be meaningful - and this would be reflected in the p-value. So, say, a correlation of 0.5 for a hundred events may be as meaningful as 0.7 for a dozen events. My problem was how to get a statistical 'feel' for this - and I think the p-value would give me this.

If I understand the advice you have kindly profffered, I am on the right path.

Than you

My concern was that a correlation needed to be higher for fewer events for it to be meaningful - and this would be reflected in the p-value. So, say, a correlation of 0.5 for a hundred events may be as meaningful as 0.7 for a dozen events. My problem was how to get a statistical 'feel' for this - and I think the p-value would give me this.