Repeated-measures design is defined as:

Repeated-measures design is defined as:

• A. A cross-sectional design with unrelated samples.
• B. A factorial design with between-subject factors.
• C. An experimental design in which the same organisms participate in all treatment conditions and so the resulting data are related (a.k.a. related design or within-subject design).
• D. An experimental design where each participant experiences only one condition.

Answer: (C)

Repeated-measures design means the same participants participate in every treatment condition, so the data are related (a within-subject design). This setup uses paired measurements from the same individuals, which reduces variability due to differences between people and often increases statistical power. It contrasts with designs that use unrelated samples (cross-sectional or between-subjects designs) and with designs where each participant experiences only one condition. A factorial design with between-subject factors involves different groups for different combinations of factors, not the same person across all conditions. The defining feature here is ongoing participation by the same individuals across all conditions, yielding related data; note that such designs can introduce order or carryover effects that researchers manage with counterbalancing.

Repeated-measures design means the same participants participate in every treatment condition, so the data are related (a within-subject design). This setup uses paired measurements from the same individuals, which reduces variability due to differences between people and often increases statistical power. It contrasts with designs that use unrelated samples (cross-sectional or between-subjects designs) and with designs where each participant experiences only one condition. A factorial design with between-subject factors involves different groups for different combinations of factors, not the same person across all conditions. The defining feature here is ongoing participation by the same individuals across all conditions, yielding related data; note that such designs can introduce order or carryover effects that researchers manage with counterbalancing.