What technique involves dividing data into groups to analyze variations within those groups?

The technique that involves dividing data into groups to analyze variations within those groups is stratification. Stratification allows for more meaningful analysis by identifying and isolating the variations that exist within different subgroups of data. This process helps in understanding how different factors or characteristics may influence the overall results.

For instance, if a researcher is analyzing customer satisfaction scores, stratification could involve categorizing responses by demographics such as age, geographic location, or purchase frequency. By doing so, the researcher can better see trends and discrepancies amongst different groups, which might be lost if the data were analyzed as a whole.

This approach is particularly beneficial in quality improvement projects and Six Sigma initiatives, as it helps teams identify specific areas that may require attention or improvement. By focusing on these variations, organizations can target their efforts more effectively, leading to better outcomes.

The other techniques mentioned do not specifically focus on grouping data to isolate variations. The Nominal Group Technique is more about facilitating group decision-making. Extrapolation involves estimating future values based on past data without specifically analyzing group variations. Correlation analysis examines the relationship between two variables but does not segment data into subgroups for analysis of variations.