"Whiter Teeth An ad for Crest Whitestrips Premium claims that the strips will whiten teeth in 7 days and the results will last for 12 months. A researcher who wishes to test this claim studies 20 sets of identical twins. Within each set of twins, one is randomly selected to use Crest Whitestrips Premium in addition to regular brushing and flossing, while the other just brushes and flosses. Whiteness of teeth is measured at the beginning of the study, after 7 days, and every month thereafter for 12 months.


What are other factors (controlled or uncontrolled) that could affect the response variable?"

Verified step by step guidance

1. Step 1: Identify the response variable in the study, which is the whiteness of teeth measured at different time points.
2. Step 2: Understand that the study uses identical twins to control for genetic factors, as twins share the same genetic makeup, reducing genetic variability in the response.
3. Step 3: Consider controlled factors, such as the use of Crest Whitestrips Premium versus regular brushing and flossing, and the random assignment of treatment within twin pairs to reduce bias.
4. Step 4: Think about uncontrolled factors that could affect teeth whiteness, such as diet (consumption of staining foods or drinks like coffee or wine), oral hygiene habits beyond brushing and flossing, exposure to other whitening products, or environmental factors like smoking.
5. Step 5: Also consider measurement-related factors, such as consistency in how whiteness is measured, lighting conditions, or subjective assessment differences, which could introduce variability in the response.

Key Concepts

Controlled Variables

Controlled variables are factors that the researcher keeps constant to ensure that the effect on the response variable is due to the treatment alone. In this study, regular brushing and flossing are controlled to isolate the effect of Crest Whitestrips. Controlling variables helps reduce confounding and increases the validity of the results.

Confounding Variables

Confounding variables are factors other than the treatment that may influence the response variable, potentially biasing the results. Examples here could include diet, smoking habits, or natural differences in tooth enamel. Identifying and accounting for confounders is essential to accurately interpret the effect of the whitening strips.

Randomization and Matched Pairs Design

Randomization assigns treatments randomly to subjects to reduce bias, while matched pairs design uses pairs (like twins) to control for genetic and environmental similarities. This design helps isolate the treatment effect by comparing outcomes within each twin pair, minimizing variability from individual differences.