Correlation between two variables shows how the two series are associated with each other. How one variable changes with the change in other

High degree of correlation implies that there is a strong relationship between the two variables and that the changes in one variable cause predictable changes in the other variable.

If high scores on one variable are paired with high scores on other variable and low scores are paired with low scores, correlation will be high and therefore Pearson's r will be high and positive.

The highest score shows high degree of correlation. It can be positive or negative.

A positive value of correlation coefficient implies that high value of one variable will correspond to a high value of another variable and vice versa.

Other factors being equal, a restricted range usually yields a smaller correlation coefficient.

Power Analysis allows us to determine the sample size required to detect an effect of a given size and it does not require the use of r.

Negative correlation implies that high value of one variable will correspond to a low value of another variable and vice versa.

When there is perfect negative correlation, the value of correlation coefficient will be -1.

Spearman's rank correlation is used for ordinal data, where the values have not been measured but have been ranked.