How to interpret Pearson’s correlation results?

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When interpreting Pearson's correlation coefficient, which measures the linear relationship between two continuous variables, there are several key points to consider:
1. Range of values: Pearson's correlation coefficient, often denoted as "r," ranges from -1 to 1. A value of -1 indicates a perfect negative linear relationship, 0 indicates no linear relationship, and 1 indicates a perfect positive linear relationship. Values between -1 and 1 reflect varying degrees of linear association.
2. Sign of the coefficient: The sign of the correlation coefficient indicates the direction of the relationship. A positive value (r > 0) suggests a positive association, meaning that as one variable increases, the other tends to increase as well. A negative value (r < 0) suggests a negative association, meaning that as one variable increases, the other tends to decrease.
3. Magnitude of the coefficient: The magnitude of the correlation coefficient indicates the strength or degree of the linear relationship. Values closer to -1 or 1 indicate a stronger linear relationship, while values closer to 0 suggest a weaker relationship. Values near 0 indicate little to no linear association.
4. Statistical significance: It is important to assess whether the correlation coefficient is statistically significant. This indicates whether the observed correlation is likely to be a true representation of the population or if it could be due to random chance.