Quasi-Experiment

A quasi-experiment is a type of research strategy used by businesses and analysts to figure out if a specific action or variable has an effect on something else, even though they don’t use full random assignment. Think of it like testing to see if a new fertilizer makes plants grow bigger, but instead of randomly choosing which plants get the new mix, they use plants from two different but similar fields. In business intelligence and data analytics, this method is useful because sometimes it’s impossible or unethical to do a true experiment where people or things are randomly assigned to different groups.

In a quasi-experiment, the business might compare the performance of one group that experienced a change to another group that didn’t. For example, a company could look at sales data from before and after they launched a new ad campaign. Or, they compare two groups that are similar, like stores in different locations, to see if changes in one store affect sales more than in the other.

This approach is like doing an experiment with training wheels. It gives businesses valuable insights, but there’s a catch; because not everything in the groups is controlled or random, it’s hard to be sure that the changes in the data are only because of the action being tested. It’s not as precise as a true experiment, but it can still provide a good idea of what might be working and what’s not, especially when real-world testing is on the table.