Accessing the Reliability of Correlation using Probable Error
To assess the reliability of a correlation coefficient, one method that can be used is the probable error. The probable error is a statistical measure that provides an estimate of the possible error in a correlation coefficient due to sampling variability. By determining the probable error, we can gain insights into the accuracy and stability of the correlation.

Calculation of Probable Error
The probable error is calculated by multiplying the standard error of the correlation coefficient by a constant value. This constant value is based on the sample size and the desired level of confidence. The formula for calculating the probable error is:
Probable Error = Standard Error of the Correlation Coefficient * Constant Value
The standard error of the correlation coefficient can be calculated using the formula:
Standard Error = sqrt((1-r^2)/(n-2))
Where r is the correlation coefficient and n is the sample size.

Interpreting Probable Error
Once the probable error is calculated, it can be compared to the correlation coefficient to determine the reliability of the correlation. If the correlation coefficient is significantly larger than the probable error, it suggests that the correlation is reliable and not due to chance. On the other hand, if the correlation coefficient is similar to or smaller than the probable error, it indicates that the correlation may not be statistically significant.

Conclusion
Using the probable error to assess the reliability of a correlation coefficient provides valuable information about the strength and stability of the relationship between variables. By understanding the probable error, researchers can make more informed decisions about the significance of correlations in their data analysis.