Contents
Why is my correlation so low?
Excess variance is probably the most common cause of smaller than expected correlations. Usually, excess variance is the result of a lack of adequate control in data generation. Inappropriate sample — Data points that look like outliers or excess variance may be sham samples.
Is 0.15 a weak correlation?
To say that a correlation of 0.15 or any other “small size” effect is “weak” or has to be non significant is not correct. Therefore, in many biological and medical studies, a correlation of 0.15 is not considered weak or non important.
What is the p value of a correlation coefficient?
The p-value is the probability of observing a non-zero correlation coefficient in our sample data when in fact the null hypothesis is true. A low p-value would lead you to reject the null hypothesis. A typical threshold for rejection of the null hypothesis is a p-value of 0.05.
What does low p value and low are value mean?
So the $r$ value is not that impressive (usually we see $r>.5$), but the $p$-value is still significant. Usually I would think a low $r$ value would mean high $p$-value (no significant correlation), or vice versa (low $p$-value would mean a high $r$ value).
What is the p value to reject the null hypothesis?
A low p-value would lead you to reject the null hypothesis. A typical threshold for rejection of the null hypothesis is a p-value of 0.05. That is, if you have a p-value less than 0.05, you would reject the null hypothesis in favor of the alternative hypothesis—that the correlation coefficient is different from zero.
Are there any false positives in correlation analysis?
So, although we would interpret the size of the differences/correlations/regression coefficients in the same way in both studies, the p-value would tell another story. Is this reasonable? Additionally, we know that p-values are very unstable especially with small sample size and we may therefore expect more false positives.