Experimental methods are used to demonstrate causal relationships between variables. In an experiment, the researcher systematically manipulates a variable of interest (known as the independent variable) and measures the effect on another variable (known as the dependent variable). Unlike correlational studies, which can only be used to determine if there is a relationship between two variables, experimental methods can be used to determine the actual nature of the relationship. That is to say that if changes in one variable actually cause another to change.
Another example is from Whorf's experience as a chemical engineer working for an insurance company as a fire inspector.  While inspecting a chemical plant he observed that the plant had two storage rooms for gasoline barrels, one for the full barrels and one for the empty ones. He further noticed that while no employees smoked cigarettes in the room for full barrels, no-one minded smoking in the room with empty barrels, although this was potentially much more dangerous because of the highly flammable vapors still in the barrels. He concluded that the use of the word empty in connection to the barrels had led the workers to unconsciously regard them as harmless, although consciously they were probably aware of the risk of explosion. This example was later criticized by Lenneberg  as not actually demonstrating causality between the use of the word empty and the action of smoking, but instead was an example of circular reasoning . Pinker in The Language Instinct ridiculed this example, claiming that this was a failing of human insight rather than language.
Retailer Creates Pregnancy Detection Model
In a near infamous retail big data example, retailer Target correlated its baby-shower registry with its Guest ID program in order to determine when a shopper is likely pregnant. Target's Guest ID is a unique consumer ID that tracks purchase history, credit card use, survey responses, customer support incidents, email click-throughs, web site visits and more. The company supplements the consumer activities it tracks by purchasing demographic data such as age, ethnicity, education, marital status, number of children, estimated income, job history and life events such as when you last moved or if you have been divorced or ever declared bankruptcy.
By comparing shoppers who registered on the baby shower registry with the purchase history from their Guest ID, the retailer discovered changes in shopping habits as the woman progressed through her pregnancy. For example, during the first 20 weeks, pregnant women began purchasing supplements like calcium, magnesium and zinc. In the second trimester, pregnant women began buying larger jeans and larger quantities of hand sanitizers, unscented lotion, fragrance free soap and cotton balls; often extra-big bags of cotton balls. In total, the retailer identified about 25 products purchased by pregnant women.
By applying these purchase behaviors to all shoppers Target was able to identify women who were pregnant even though these women had not notified Target – or often anybody else – they were pregnant. Target used this discovery to create a pregnancy prediction model which assigned a pregnancy prediction score to shoppers. The retailer was then able to distribute baby product promotions to a very specific customer segment, timed to stages of pregnancy, and the financial results were off the charts. Not only did these women make new baby product purchases, but knowing that significant life events change a consumers overall shopping habits, Target was able to grow its revenues from $44 billion in 2002 when the analysis started to $67 billion in 2010. While the retailer does not publicly comment on this program, Target's president, Gregg Steinhafel, is on record sharing with investors that the company's "heightened focus on items and categories that appeal to specific guest segments such as mom and baby" heavily contribute to the retailers success.
Notwithstanding the consumer privacy and public relations considerations which must be deliberated, this is a powerful lesson for retailers.