A dependent variable (called the response variable in AP Statistics) is the outcome a researcher measures or observes in a study to see whether it changes when the independent (explanatory) variable changes, such as plant growth measured under different fertilizer treatments.
A dependent variable is the thing you measure at the end of a study. It's the outcome, the result, the number you actually write down. It's called "dependent" because its value is thought to depend on something else, namely the independent variable that you manipulate or compare across groups.
Here's the translation tip that saves AP Stats students constantly: the official AP language is response variable (dependent) and explanatory variable (independent). Same idea, different labels. If a researcher tests three doses of fertilizer and measures plant height, the dose is the explanatory/independent variable and the height is the response/dependent variable. Per Topic 1.1, those measured numbers only mean something in context. "42" tells you nothing, but "42 cm of plant growth under the high-fertilizer treatment" is data you can analyze.
This term lives in Topic 1.1 (Introducing Statistics) inside Unit 1, supporting learning objective AP Stats 1.1.A, which asks you to identify questions to be answered based on variation in data. Every statistical question is secretly a question about a dependent variable. "Does fertilizer affect growth?" really means "does the dependent variable (growth) vary with the independent variable (fertilizer)?" The essential knowledge here is that numbers convey meaning only in context, and the dependent variable IS that context. It tells you what the numbers measure.
Beyond Unit 1, this concept never goes away. In Unit 2 it becomes the y-variable in regression. In Unit 3 it's the response you measure on experimental units. In Units 6-9 it's the quantity your hypotheses make claims about. Get this straight now and half of experimental design becomes vocabulary you already own.
Keep studying AP Statistics Unit 1
Independent Variables (Units 1 & 3)
These two are a matched pair. The independent (explanatory) variable is what gets changed or compared, and the dependent (response) variable is what gets measured to see the effect. You can't define one without the other, and exam questions love asking you to identify both in a study description.
Experimental Units (Unit 3)
Experimental units are the who or what being studied (people, plants, melons), and the dependent variable is what you measure on each one. In the 2017 FRQ about melon distributors, each melon is a unit and its diameter is the measured variable.
Control Variables (Unit 3)
Control variables are held constant so they can't muddy the relationship you care about. The whole point of controlling is to make sure changes in the dependent variable can be credited to the independent variable and not to something else.
Hypothesis (Units 6-9)
Every null and alternative hypothesis you'll write in inference is a claim about a dependent variable, like the mean response or the proportion of successes. The variable you measure in Unit 1 becomes the parameter you test in Unit 6 and beyond.
Multiple-choice questions test this as straight identification. You'll get a study description and a stem like "Which variable is being measured or observed?" and you have to pick out the dependent variable while dodging the independent variable and any controls. The trap answers are always the manipulated variable and the held-constant variables, so sort all three roles before answering.
On FRQs, the term shows up inside experimental design and data analysis prompts. The 2017 exam, for example, described melon diameters measured from two distributors, and strong answers kept the measured variable in context (diameter of melons, in specific units) rather than talking about bare numbers. Whenever you describe a distribution or design an experiment, name the dependent/response variable explicitly with its units. Graders look for it.
The independent variable is the cause side (what's manipulated or compared), and the dependent variable is the effect side (what's measured). Quick test: ask "what did the researcher change?" (independent) versus "what did the researcher record?" (dependent). In "does caffeine affect reaction time," caffeine dose is independent and reaction time is dependent. Also remember AP Stats usually says explanatory and response instead, so don't panic when the exam swaps labels.
The dependent variable is the outcome a researcher measures, and it's expected to change in response to the independent variable.
AP Statistics usually calls the dependent variable the response variable and the independent variable the explanatory variable, so know both sets of names.
A dependent variable only carries meaning in context, so always state what is measured and in what units, like "diameter of melons in centimeters."
In regression (Unit 2), the dependent variable is the y-variable that you predict from the explanatory x-variable.
In experiments (Unit 3), the dependent variable is what you measure on each experimental unit after applying treatments.
Showing that a treatment causes a change in the dependent variable requires a well-designed experiment, not just an observed association.
It's the outcome a researcher measures in a study, like test scores, plant height, or melon diameter. Its value is expected to depend on the independent variable, which is what the researcher changes or compares.
Yes. "Response variable" is the term AP Statistics prefers, and "dependent variable" is the term you probably learned in science class. They mean the same thing, and the exam expects you to recognize both.
Ask what the researcher changed versus what the researcher measured. The changed or compared thing (fertilizer dose, drug vs. placebo) is independent; the measured outcome (growth, recovery time) is dependent.
Not by itself. A change in the dependent variable only supports a causal conclusion if it came from a well-designed experiment with random assignment. In an observational study, the same change might be explained by a confounding variable.
No. A dependent variable can be quantitative (reaction time in seconds) or categorical (whether a patient recovered, yes or no). What makes it dependent is its role as the measured outcome, not the type of data.