Let’s Summarize

There are four steps in a statistical investigation:
- Ask a question that can be answered by collecting data.
- Decide what to measure, and then collect data.
- Summarize and analyze.
- Draw a conclusion, and communicate the results.
There are two types of statistical research questions:
- Questions about a population
- Questions about cause-and-effect
To answer a question about a population, we select a sample and conduct an observational study. To answer a question about cause-and-effect we conduct an experiment.
There are two types of statistical studies:
- Observational studies: An observational study observes individuals and measures variables of interest. We conduct observational studies to investigate questions about a population or about an association between two variables. An observational study alone does not provide convincing evidence of a cause-and-effect relationship.
- Experiments: An experiment intentionally manipulates one variable in an attempt to cause an effect on another variable. The primary goal of an experiment is to provide evidence for a cause-and-effect relationship between two variables.
In statistics, a variable is information we gather about individuals or objects.
When we investigate a relationship between two variables, we identify an explanatory variable and a response variable. To establish a cause-and-effect relationship, we want to make sure the explanatory variable is the only thing that impacts the response variable. Other factors, however, may also influence the response. These other factors are called confounding variables.
The influence of confounding variables on the response variable is one of the reasons that an observational study gives weak, and potentially misleading, evidence of a cause-and-effect relationship. A well-designed experiment takes steps to eliminate the effects of confounding variables, such as random assignment of people to treatment groups, use of a placebo, and blind conditions. For this reason, a well-designed experiment provides convincing evidence of cause-and-effect.

Content by the Open Learning Initiative and licensed under CC BY.