Experimental Design (Variables & Controls)
Map the experiment before you read the choices. In ACT Science design questions, the fastest path is to mark what changed, what was measured, and what was kept the same.
Core Idea
Experimental design questions become manageable once you build a quick experiment map: what changed on purpose, what was measured, what stayed fixed, and which comparison counts as the baseline.
Understanding
Most Scientific Investigation items sit inside a short experiment summary. The test usually is not asking for outside science facts. It is asking whether you can track the job each part of the setup is doing.
- Map the setup first: what was changed, what was measured, and what stayed fixed?
- Name the comparison: which group is the baseline, and which group differs in the tested factor?
- Then read the change: if the passage adds a new trial or modifies a step, ask whether that change sharpens the test or weakens it.
- What exactly changed?
- Does that change isolate the same variable more clearly, or does it add noise?
That is the spine of this unit. Once the map is clear, variable questions, procedure questions, prediction questions, and limitation questions all become easier to sort.
Concept Guides
6Identify hypotheses, independent/dependent variables, and control conditions.
Name each part by its job. Do not guess from whatever number looks most important.
Understand procedures and experimental tools described in passages.
Read tools by function. Ask what each step or piece of equipment is doing in the procedure.
Predict outcomes of additional trials or procedural changes using given patterns.
Extend the shown pattern, not your intuition. Use the trend in the passage and keep the rest of the
Evaluate the role of controls and constants in isolating variables.
Constants protect the comparison. They keep the experiment focused on one tested difference.
Interpret experimental results and connect them to the stated purpose/hypothesis.
Match the conclusion to the exact claim. The best answer uses the data without overstating what they
Recognize sources of error, limitations, and the need for replication at a conceptual level.
One result is not always a reliable pattern. Replication helps separate a real effect from random va