In AP Statistics, a single-blind experiment is one where either the subjects don't know which treatment they're receiving but the researchers do, or the researchers measuring responses don't know but the subjects do. Only one side is blinded, which controls some bias but not all of it.
A single-blind experiment hides the treatment assignments from exactly one group of people involved in the study. Usually that means the subjects don't know whether they got the real treatment or the placebo, while the research team does. But the CED is explicit that it works the other way too. If the participants know their treatment but the people measuring the response variable don't, that's still single-blind.
The point of blinding is bias control. If subjects know they're getting the "real" treatment, their expectations can change how they respond. If researchers know who got what, they might (even unconsciously) measure or evaluate responses differently. Single-blinding closes one of those two doors. The catch, and the limitation AP questions love to poke at, is that the unblinded side can still introduce bias. A single-blind design where only subjects are blinded still leaves room for researcher bias when measuring outcomes.
Single-blind experiments live in Topic 3.5 (Introduction to Experimental Design) in Unit 3: Collecting Data. The term comes straight from learning objective AP Stats 3.5.C, which asks you to compare experimental designs and methods. It also supports 3.5.B, since controlling potential confounding variables is one of the four marks of a well-designed experiment, and blinding is a tool for that control. Unit 3 is where the exam tests whether you understand that how data is collected determines what conclusions you can draw. Knowing exactly who is blinded, and what bias that does or doesn't eliminate, is the kind of precise reasoning experimental design questions reward.
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Double-Blind Experiment (Unit 3)
A double-blind experiment blinds both sides at once. Neither the subjects nor the researchers interacting with them or measuring responses know who got which treatment. Think of single-blind as closing one door to bias and double-blind as closing both.
Placebo (Unit 3)
Blinding usually only works if there's a placebo. Subjects can't be kept in the dark about their treatment unless the control group gets something that looks and feels identical to the real thing.
Confounding Variable (Unit 3)
Blinding exists to fight bias from expectations, which can act like a confounding influence on the response variable. If unblinded subjects respond better just because they believe they got the treatment, you can't tell whether the treatment or the belief caused the change.
Random Assignment (Unit 3)
Random assignment and blinding solve different problems, and a well-designed experiment often uses both. Random assignment balances out differences between groups before treatments start, while blinding controls bias in how people respond and how responses get measured afterward.
Single-blind shows up mostly in multiple-choice questions that describe a study scenario and ask you to classify the design or identify its limitation. A classic stem looks like this: students don't know whether they're in the experimental or control group, but their teachers do know which method they're using. You'd need to name that single-blind, not double-blind. Other questions flip the direction (researchers evaluating stress levels are blinded but participants aren't) or ask when a single-blind setup is most appropriate, or what bias it fails to eliminate. On FRQs about experimental design, you may be asked to describe or improve a design, and correctly explaining who is blinded and why earns points. The trap to avoid is vagueness. Saying "the study is blinded" isn't enough; say exactly which group doesn't know the treatment assignments and what bias that prevents.
In a single-blind experiment, exactly one group (either the subjects or the researchers measuring responses) doesn't know the treatment assignments. In a double-blind experiment, neither group knows. The fast check on a multiple-choice question is to count who's in the dark. If only one side is blinded, it's single-blind, even if the blinded side is the researchers rather than the subjects.
A single-blind experiment hides treatment assignments from either the subjects or the researchers, but not both.
Single-blind can go either direction, so a study where researchers measuring responses are blinded but participants know their treatment still counts as single-blind.
The limitation of blinding only the subjects is that researchers can still bias the results when they measure or evaluate responses.
Blinding controls bias from expectations, while random assignment balances confounding variables between groups; a strong design uses both.
On the exam, always state exactly who is blinded and what specific bias that blinding prevents, because vague answers don't earn credit.
This term comes from Topic 3.5 (Introduction to Experimental Design) and learning objective AP Stats 3.5.C on comparing experimental designs.
It's an experiment where either the subjects don't know which treatment they're receiving but the research team does, or vice versa. Only one side is blinded, which is the defining feature.
No, and this is a common trap. The CED definition works in both directions, so a design where the researchers measuring responses are blinded while participants know their own treatment is also single-blind.
Single-blind keeps the treatment assignments hidden from one group (subjects or researchers); double-blind hides them from both. Double-blind controls more bias because neither side's expectations can affect the responses or the measurements.
The unblinded side can still introduce bias. If only subjects are blinded, researchers who know the assignments might unconsciously measure or evaluate responses differently between treatment groups, which is exactly the limitation multiple-choice questions ask about.
Yes. It's part of Topic 3.5 in Unit 3 and falls under learning objective AP Stats 3.5.C, which asks you to compare experimental designs. Expect multiple-choice scenarios where you classify a design as single-blind, double-blind, or neither.
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