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The main problem with spurious correlations is that we typically do not know what the "hidden" agent is. However, in cases when we know where to look, we can use partial correlations that control for (partial out) the influence of specified variables.
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Are correlation coefficients "additive. For example, an average of correlation coefficients in a number of samples does not represent an "average correlation" in all those samples. In cases when you need to average correlations, they first have to be converted into additive measures. For example, before averaging, you can square them to obtain coefficients of determination, which are additive (as explained before in this section), or convert them into so-called Fisher z values, which are also additive.
How to Determine Whether Two Correlation Coefficients are Significant. A test is available that will evaluate the significance of differences between two correlation coefficients in two samples. The outcome of this test depends not only on the size of the raw difference between the two coefficients but also on the size of the samples and on the size of the coefficients themselves. Consistent with the previously discussed principle, the larger the sample size, the smaller the effect that can be proven significant in that sample.
In general, due to the fact that the reliability of the correlation coefficient increases with its absolute value, relatively small differences between large correlation coefficients can be significant.
For example, a difference of. To index Purpose, Assumptions. The t-test is the most commonly used method to evaluate the differences in means between two groups. For example, the t-test can be used to test for a difference in test scores between a group of patients who were given a drug and a control group who received a placebo.
Theoretically, the t-test can be used even if the sample sizes are very small (e.
As mentioned before, the normality assumption can be evaluated by looking at the distribution of the data (via histograms) or by performing a normality test. The equality of variances assumption can be verified with the F test, or you can use the more robust Levene's test. If these conditions are not met, then you can evaluate the differences in means between two groups using one of the nonparametric alternatives to the t- test (see Nonparametrics and Distribution Fitting).
The p-level reported with a t-test represents the probability of error involved in accepting our research hypothesis about the existence of a difference.
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