If you follow fitness or nutrition research, you’ve probably heard the common phrase, “Correlation does not equal causation.” While this is true, it does not render all correlations meaningless. Correlations are commonly misinterpreted, which results in situations where people make leaps to unsupported conclusions, or miss out on important information by dismissing correlations altogether. But, with a little bit of background information about how correlations work, these common mistakes can be avoided.

### What is a correlation?

Put simply, two values will be correlated if they vary together. For instance, consider height and weight. Taller people tend to weigh more, so weight goes up as height goes up. This is an example of a positive correlation, because the values vary in the same direction. You can also have a negative or inverse correlation, in which one variable goes down as the other goes up (e.g., the amount of weight you’re lifting, and the number of reps you can complete).

Let’s go back to the correlation between height and weight. This relationship may be true in general, but there are certainly cases of short people that are very heavy and tall people that are very light; these cases don’t follow the general pattern, which decreases the strength of the correlation. In most studies you see, the strength of the correlation between two variables is indicated by the Pearson product-moment correlation coefficient, denoted as R. This coefficient R can take any value ranging from -1 to 1. A weaker correlation results in an R value closer to 0, whether positive or negative (see Figure 1). Conversely, a stronger correlation results in an R value closer to -1 (strong negative correlation) or 1 (strong positive correlation). Correlations will also be reported with a p-value, which pertains to statistical significance. If the p-value is less than or equal to 0.05, correlations are generally considered “statistically significant,” which is a statistician’s way of saying that the R value of the correlation is reliable. P-values, which range from 0 to 1, can be interpreted as follows:

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