If you’ve taken a science class, you’re probably familiar with the term “data.” Data is information that scientists collect in their studies, like facts and statistics. Scientists analyze this data and review the results for important conclusions or possible new areas of research.
Data is a big deal in science. Data helps scientists understand how and why things happen—very important things like drug use patterns (also called trends), the ways your body and brain work, the causes of and treatments for diseases, and much more.
Scientists use different types of studies to collect data. Most types of studies fall into two categories: experimental and observational.
In these studies, scientists randomly assign people to two or more groups and study the main differences between the groups.
For example, people who smoke could be assigned to two groups:
- Group 1 receives an experimental medicine for reducing their desire to smoke.
- Group 2 receives a placebo. A placebo looks like a real drug (a pill, for instance), but unlike a real drug, it doesn't contain active ingredients.
The scientists compare the data from the two groups to see if the medicine made a difference.
Scientists don’t control the variables in observational studies; they just observe (watch) the subjects’ behavior in an organized way. For example, a study might analyze the exercise habits of young adults who smoke daily versus young adults who don’t smoke at all.
Observational studies include case-control, cross-sectional, and longitudinal studies:
- In a case-control study (like this one), scientists look back over time and compare two groups’ experiences in connection with diseases, risky behaviors, or other factors.
- Cross-sectional studies observe a group at a single point in time. One example is NIDA’s Monitoring the Future (MTF) study, which surveys a different group of teens every year. In 2018, more than 44,000 students in grades 8, 10, and 12 participated in MTF.
- A longitudinal study observes a single group but stays with that same group over a long period of time. A good example is NIDA’s ABCD Study. It began with 11,000 9- and 10-year-olds and will follow them for more than 10 years.
Scientists look for trends or patterns. Trends can tell scientists something they didn’t know before. For example, scientists noted a dramatic trend in the 2018 MTF data: There was a major increase in teen vaping compared with 2017.
Next, scientists usually ask: Why is the trend happening? When scientists noticed a decline in teens' cigarette smoking, they dug deeper. They connected the dots between this decline and an increase in information about the risks of smoking—thanks to anti-smoking campaigns like the Centers for Disease Control and Prevention’s "Tips From A Fomer Smoker."
Data can lead to new programs and insights that help people, including teens, make smart choices for their health.