ACT Science: Research Summaries

Research Summaries questions on the ACT Science section measure a specific kind of reading: the ability to understand an experiment from a short write-up, recognize how it was designed, interpret results, and draw conclusions that match the evidence. You are not being tested on obscure science facts. You are being tested on whether you can read scientific information efficiently and reason from data.

These passages typically describe one or more experiments or studies. They may include brief methods, a table or graph, and notes about what was measured. Your job is to connect the design to the outcomes without overthinking. The test rewards disciplined, evidence-based answers.

What “Research Summaries” passages look like

A Research Summary usually includes:

• A short introduction stating the purpose of the study
• A description of the procedure (what was done and how)
• Definitions of key terms, conditions, or measurements
• A results section shown in figures, tables, or narrative form
• Sometimes multiple experiments (Experiment 1, Experiment 2, etc.) that build on each other

The content can come from biology, chemistry, physics, or Earth science, but the skills are consistent across topics.

Common question types

Most questions fall into a few repeatable categories:

• Identify the independent and dependent variables
• Identify controlled variables (constants)
• Interpret a graph or table
• Compare results across experiments or conditions
• Predict what would happen under a new condition (a cautious extrapolation)
• Evaluate a conclusion: supported, not supported, or irrelevant based on the results

Experimental design: what to look for first

Before diving into numbers, anchor yourself in the structure of the experiment. A strong mental map prevents mistakes later.

Independent vs. dependent variables

The independent variable is what the researchers intentionally change. The dependent variable is what they measure as an outcome.

A practical way to identify them:

• Ask, “What did they vary between trials or groups?” That is usually the independent variable.
• Ask, “What did they record, measure, or calculate?” That is usually the dependent variable.

If a table lists “Temperature (°C)” across the top and “Reaction rate (units)” down the side, temperature is being varied and reaction rate is being measured. Even without background knowledge, the layout often reveals roles.

Controls and constants

Controlled variables are conditions kept the same so the test focuses on the independent variable. ACT passages often state these explicitly: same sample size, same volume, same starting concentration, same light intensity, same measurement method.

Questions may ask what was controlled, or why a control group exists. A control group is the baseline that does not receive the experimental treatment. It lets you attribute differences to the variable being tested rather than to noise or outside factors.

Trials, sample size, and repetition

If the passage mentions multiple trials, averages, or repeated measurements, it is signaling reliability. You do not need to compute statistical significance, but you should respect the logic: repeated measurements reduce random error.

Averages may appear in tables. Treat them as the value to compare unless the question asks about individual trials.

Reading results: how to extract meaning from graphs and tables

Research Summaries are data-heavy, but the ACT keeps the math manageable. What matters is accurate reading.

Read the axes and units before reading trends

Many wrong answers come from skipping labels. Always confirm:

• What each axis represents
• Units (mL vs. L, seconds vs. minutes, °C vs. K)
• Scale patterns (non-zero baselines, uneven increments, logarithmic-like spacing)

A graph that starts at 50 instead of 0 can make small differences look dramatic. The ACT may use this to test whether you rely on visual impressions instead of values.

Identify trends with careful language

When describing results, stick to what the data show:

• “As X increases, Y increases” (positive association)
• “As X increases, Y decreases” (negative association)
• “Y increases then plateaus”
• “No clear change in Y across X”

Avoid assuming cause unless the design supports it. An experiment that manipulates X and measures Y can justify causal language more than a purely observational setup.

Compare groups using the same reference point

If a question asks which condition produced the highest value, find the maximum on the dependent variable scale and note the corresponding condition. If it asks for differences at a specific level of X, compare vertically at that point rather than scanning the whole graph.

When two lines cross, “which is higher” depends on the X-value. The test often targets this detail.

Drawing conclusions: what is supported by the experiment?

ACT Science rewards conservative reasoning. Conclusions must match the evidence and the design.

Supported vs. not supported

A conclusion is supported when:

• The trend is shown consistently in the data
• The dependent variable changes in response to the independent variable
• The experiment includes relevant conditions that test the claim

A conclusion is not supported when:

• It claims something outside the tested range (extrapolation too far)
• It introduces a new variable that was not measured
• It reverses cause and effect
• It ignores contradictory data points

If the experiment tested temperatures of 10°C to 40°C, claiming what happens at 90°C is speculative. A safer inference is limited to the measured range.

Correlation is not automatically causation, but experiments matter

Not every passage is purely correlational. If researchers actively manipulate a variable and hold others constant, the design supports causal inference. If the passage describes natural observations (for example, measuring plant growth in different locations without controlling sunlight or water), causal claims are weaker.

The ACT will not require philosophical debates about causation, but it will expect you to notice whether the setup actually isolates the variable.

Watch for “could,” “may,” and “most likely”

When the test uses cautious phrasing, it is inviting a cautious answer. If the data show a clear trend, the correct option may still use modest language, because scientific writing avoids overclaiming.

Multiple experiments: how to handle comparisons

Research Summaries often include several experiments with slightly different procedures. Your job is to track what changed and why.

Keep a quick ledger of differences

As you read, note in your head (or on scratch paper) what distinguishes each experiment:

• Different independent variables
• Different measurement methods
• Different starting conditions
• Different organisms/materials

Then, when a question asks why results differ, you can point to the one procedural change that plausibly explains it.

Use one experiment to interpret another

Sometimes Experiment 2 exists to test a hypothesis raised by Experiment 1. Questions may ask what Experiment 2 was trying to confirm, or which result from Experiment 1 motivated the follow-up.

In that case, look for language like “to determine whether,” “to test the effect of,” or “because the researchers suspected.”

Variables in practice: the simplest way to answer variable questions

Variable questions feel technical, but they are usually straightforward if you use the passage’s own wording.

A reliable method

1. Locate the sentence that describes what was changed between setups.
2. Locate the sentence that describes what was measured.
3. Match those directly to the answer choices.

If the passage says, “The concentration of solution A was varied,” that is the independent variable even if other things were adjusted for setup (like stirring). If it says, “The time to completion was recorded,” that is the dependent variable even if intermediate observations occurred.

Beware of “conditions” that are actually constants

The ACT likes to list many experimental details. Only some are variables. If a detail stays the same in all trials, it is not an independent variable. For example, “All samples were kept at 25°C” indicates temperature is controlled, not tested.

Practical pacing: how to stay efficient

Research Summaries can be time-consuming if you read like a textbook. A better approach is purposeful reading.

Read in layers

• First pass: purpose and setup (what is being tested and measured)
• Second pass: results (where the data are and what they show)
• Then go directly to questions, returning to the figure or method only as needed

You do not need to memorize the passage. Treat it as a reference document.

Let the questions guide you

Many questions point you to a specific figure, table, or experiment number. When that happens, narrow your attention to that section. Broad rereading wastes time and increases confusion.

Eliminate answers that go beyond the data

If an option introduces a mechanism not discussed (for example, a biochemical explanation when none was tested), it is often wrong. Research Summaries focus on what was measured, not on deep theoretical causes.

What ACT Science is really testing here

Research Summaries questions assess scientific literacy: understanding experimental design and results. If you can identify variables, read units correctly, compare conditions, and choose conclusions that stay inside the evidence, you can score well even when the topic is unfamiliar.

Approach each passage like a scientist would: define the question, track what changed, observe what happened, and refuse to claim more than the data support. That mindset, more than memorized content, is what the ACT Science section is built to reward.