**Interpreting the Results**

Correlations determine how strongly two datasets are related to each other. In this case, two metrics are compared and a 'correlation coefficient' is calculated. The correlation coefficient value indicates the degree of the relationship between the two variables, e.g., as one variable increases, the other increases by a certain degree.

**Correlation Coefficient Explanation**

The correlation coefficient ranges from -1 to 1.

-1 indicates a perfect negative correlation between the two datasets

0 indicates no correlation between the two datasets

1 indicates a perfect positive correlation between the two datasets

Interpreting the correlation coefficient can be case dependent, but some generally accepted thresholds are:

Greater than .8 is a very strong correlation

Between .8 and .6 is a moderate correlation

Less than .6 is a weak correlation

Near zero is barely any correlation

**P-value Explanation**

The p-value is essentially the chance that the reported correlation coefficient is a result of a completely uncorrelated dataset. In other words: the lower the p-value, the higher the chance that the correlation coefficient is accurate. Interpreting the p-value is also case dependent, but the generally accepted threshold is:

Greater than .05 is not statistically significant

Less than (and equal to) .05 is statistically significant