There are several statistical methods that can be used to detect a relationship between two metrics. Some commonly used methods include: 

Pearson's correlation coefficient: This measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where -1 indicates a perfect negative linear relationship, 0 indicates no relationship, and 1 indicates a perfect positive linear relationship.

Spearman's rank correlation coefficient: This is similar to Pearson's coefficient, but it is used when the two variables are not necessarily linearly related. It also ranges from -1 to 1, with the same interpretation as Pearson's coefficient.

Chi-squared test: This is a statistical test that is used to determine whether there is a relationship between two categorical variables. It calculates the difference between the observed frequencies of the variables and the expected frequencies if there was no relationship, and determines whether the difference is statistically significant.

T-test: This is a statistical test that is used to determine whether there is a significant difference between the means of two groups. It can be used to compare two metrics if the data can be divided into two groups based on some criteria.

In general, the appropriate method to use depends on the type of data and the nature of the relationship between the two metrics. It's important to carefully consider the data and the research question at hand before choosing a statistical method.