A company tests two website designs in a between-subjects design. Each version is randomly shown to 12 website visitors. If you were evaluating the validity of this experiment, which type of validity would you be most concerned about?

Question

BlackTom AI · Accepted Answer

Random assignment with only 12 participants per condition may not eliminate confounds, so internal validity is the greatest concern.

1. Relevant knowledge points and concepts
- Internal validity: whether observed differences between groups can be attributed to the manipulated independent variable (website design) rather than to confounding factors.
- Between-subjects design: different participants experience each condition; random assignment aims to equate groups on nuisance variables.
- Sampling variability and small-sample effects: with small n, randomization is less likely to balance participant characteristics across groups.
- Other validities: external (generalizability), construct (does the measure capture the intended construct), statistical conclusion (correctness of statistical inference, e.g., power, Type I/II errors).

2. Step-by-step reasoning
- The experiment randomly assigns 24 visitors into two groups of 12 each. Random assignment is intended to control confounds.
- However, with only 12 participants per group, chance imbalances in key covariates (age, device type, prior exposure, browsing context, time of day) are likely.
- Such imbalances can produce systematic differences in outcomes (e.g., click-through, time on page) that are unrelated to the design itself.
- Because the design is between-subjects, each participant only contributes to one condition, so any preexisting group differences directly affect the comparison.

3. Intermediate steps / illustrative logic
- Suppose device type strongly affects behavior and, by chance, 9 of 12 in Design A are mobile users while only 4 of 12 in Design B are mobile users. That imbalance could create an apparent difference attributed to design but actually driven by device—an internal validity threat.
- Small sample size increases sampling error: the expected equivalence from randomization relies on larger samples (law of large numbers).

4. Why the correct answer is correct
- Internal validity is foremost because the experiment’s ability to draw causal inference (that design caused the observed difference) is jeopardized by likely group imbalances and uncontrolled confounds in such small groups. Without internal validity, any further claims or generalizations are invalid.

5. Why other options are less appropriate
- External validity: important (24 visitors may not represent the population), but establishing internal validity comes first—if results aren’t causally valid, generalizability is moot.
- Construct validity: relevant if the metrics poorly capture "better design," but the more immediate risk given small groups is confounding, not measurement per se.
- Statistical conclusion validity: small sample can reduce power and increase error rates, which is a concern, but the central threat to causal inference in this setup is internal validity due to potential group differences.

6. Conclusion confirming the answer
Given the small between-subjects groups and the high risk of uncontrolled group differences, internal validity is the primary concern. This matches the provided correct answer: Internal validity.

A company tests two website designs in a between-subjects design. Each version is randomly shown to 12 website visitors. If you were evaluating the validity of this experiment, which type of validity would you be most concerned about?Single choice

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