Understanding Confounding in Experiments

This video tutorial introduces the concepts of experiments and confounding, contrasting them with observational studies. It explains how experiments involve applying a treatment to subjects and measuring the effects, while observational studies rely on observing and measuring without intervention. The video provides examples of experiments and highlights the issue of confounding, where multiple variables may affect the outcome, making it unclear which is responsible. The tutorial concludes with a preview of the next lesson on using control groups to reduce confounding.

What is the primary difference between an observational study and an experiment?

• Observational studies are based on observations without interventions.

• Experiments are based on surveys and polls.

• Experiments do not involve any treatments.

• Observational studies involve applying treatments.

Which of the following is an example of an experiment?

• Measuring the height and weight of gym members.

• Surveying people about their favorite food.

• Testing a new laundry detergent by using it.

• Observing traffic patterns in a city.

In the context of experiments, what is a 'treatment'?

• A factor applied to subjects to observe effects.

• A method of collecting observational data.

• A survey conducted on a sample.

• A statistical analysis technique.

Why is it important to isolate the treatment in an experiment?

• To make the experiment more complex.

• To reduce the cost of the experiment.

• To increase the sample size of the study.

• To ensure the treatment is the only variable affecting the outcome.

What is confounding in the context of experiments?

• A scenario where multiple factors make it unclear which caused the effect.

• A situation where the treatment is clearly identified.

• A study that uses only observational data.

• An experiment with a large sample size.

Which of the following is an example of confounding?

• A study where only one variable is tested.

• A company claims a gas additive improves mileage, but drivers also change driving habits.

• A survey conducted without any interventions.

• An experiment with a clear and isolated treatment.

What can be a downfall of many experiments if not properly addressed?

• Large sample sizes.

• Confounding variables.

• Clear treatment isolation.

• Simple experimental design.

What will the next lesson focus on to help reduce confounding?

• Reducing the sample size.

• Conducting more surveys.

• Using control groups.

• Increasing the number of treatments.