One main staple of research studies is called hypothesis testing. A *hypothesis test* is a technique for using data to validate or invalidate a claim about a population. For example, a politician may claim that 80% of the people in her state agree with her — is that really true? Or, a company may claim that they deliver pizzas in 30 minutes or less; is that really true? Medical researchers use hypothesis tests all the time to test whether or not a certain drug is effective, to compare a new drug to an existing drug in terms of its side effects, or to see which weight-loss program is more effective with a certain group of people.

The elements about a population that are most often tested are

The population mean (Is the average delivery time of 30 minutes really true?)

The population proportion (Is it true that 80% of the voters support this candidate, or is it less than that?)

The difference in two population means or proportions (Is it true that the average weight loss on this new program is 10 pounds more than the most popular program? Or, is it true that a greater proportion of males experience the side effects of a drug than females do?)

Hypothesis tests are used in a host of areas that affect your everyday life, such as medical studies, advertisements, polling data, and virtually anywhere that comparisons are made based on averages or proportions. And in the workplace, hypothesis tests are used heavily in areas like marketing, where you want to determine whether a certain type of ad is effective or whether a certain group of individuals buys more or less of your product now compared to last year.