A study was conducted looking at the association between number of polyps in the colon and mean grams of fiber consumed per day. Which form of correlation would be more appropriate to report if at least one variable does not meet the distribution assumption required to use Pearson correlation?

When analyzing the relationship between two variables, the choice of correlation method depends on the underlying distribution of those variables. The Spearman correlation is often more appropriate when at least one of the variables does not meet the normality assumption required for the Pearson correlation.

The Pearson correlation measures the linear relationship between two continuous variables and assumes that both variables are normally distributed. If these assumptions are violated, the results can be misleading. In contrast, the Spearman correlation is a non-parametric measure that assesses how well the relationship between two variables can be described using a monotonic function. This makes it suitable for ordinal data or for continuous data that do not meet the assumptions of normality because it relies on ranked values rather than raw data.

In this particular scenario, since it is specified that at least one of the variables does not meet the distribution assumption needed for the Pearson correlation, the correct choice is to report the Spearman correlation results. This method is robust against violations of normality and will provide a more reliable indication of the association between the number of polyps and fiber consumption.

Choosing the Spearman correlation effectively accounts for potential skewness or outliers in the data that might affect the results of a Pearson correlation analysis, thus yielding a more valid understanding of the