AP, SAT and ACT · Sciences

ACT Science review: key skills

ACT Science skill cards: reading tables and graphs, variables and controls, interpolation, trends, conflicting viewpoints and claims, with mini datasets.

35 cards5 topicsAbout 15 minutes a roundACT style science reasoning preparation (data interpretation, experimental design and evaluating claims)

Independent study material written by StudyBrix. AP and SAT are trademarks of College Board and ACT is a trademark of ACT, Inc.; neither organisation is involved with or has reviewed this deck.

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About this deck

This deck is for students preparing for the science section of the ACT and for anyone who wants sharper scientific reasoning. On the current version of the ACT, science is an optional section of 40 multiple-choice questions in 40 minutes, built around passages from biology, chemistry, earth and space science and physics. The questions test reasoning more than memorized facts: reading tables, graphs and diagrams, understanding experimental design, and judging whether evidence supports a claim or model. Check whether the colleges you are applying to want the science score before you plan your preparation.

No specific science background is required beyond typical high school courses, but you should be comfortable with percentages, ratios, simple averages and units. Each card comes with a small original dataset; answer the question it implies before you flip.

Practise reading the axes, units and legend of every figure before looking at a question, then go straight to the relevant figure instead of rereading the whole passage. For experiments, always ask: what was changed, what was measured, and what was held constant? For passages with competing explanations, summarize each viewpoint in one line.

Common mistakes: reading the wrong row, column or curve in a busy figure; assuming a trend continues far beyond the data shown; and treating a correlation as proof of cause. Another frequent error is choosing an answer that is scientifically true but not supported by the passage. Pacing is the other trap: with about a minute per question, do not get stuck on one hard item.

Before you start: Typical high school science and math: percentages, ratios, averages, units and reading simple graphs.

All 35 cards

Cover the definitions with a sheet of paper and test yourself, or use the study modes above.

Variables and experimental design

Independent variable (experiments): The factor the experimenter deliberately changes between trials.Example: Plants are given 0, 5 or 10 mL of fertilizer per week; fertilizer amount is what changes.
Dependent variable (experiments): The quantity measured in each trial to see how it responds to the changed factor.Example: In the fertilizer study, plant height after 4 weeks.
Controlled variables: Conditions kept the same across all trials so they cannot explain differences in the results.Example: Same pot size, soil, light and water for every plant.
Control group: A trial that receives none of the treatment, used as a baseline for comparison.Example: The 0 mL fertilizer plants show growth without the treatment.
Designing a fair test: Change only one factor at a time and keep everything else constant, so any difference can be traced to that factor.Example: To test temperature on reaction time, run 20, 30 and 40 C with the same concentration and volume.
Repeated trials: Running each condition several times and averaging reduces the effect of random error and shows whether results are consistent.Example: Trial times 4.1, 4.3 and 4.2 s average to 4.2 s.
Testable hypothesis: A prediction that an experiment could support or contradict with measurable data.Example: If salt concentration increases, then the boiling point of water will rise.
Comparing two experiments: Find which condition differs between the setups; that difference is what the comparison can test.Example: Exp 1: 25 C, 1 M acid. Exp 2: 25 C, 2 M acid. Only concentration changed.

Reading tables and graphs

Reading axes, units and scale: Before using a figure, note what each axis measures, its units and the size of each gridline step.Example: A y-axis in kPa with gridlines every 50 kPa: a point midway between 200 and 250 is about 225 kPa.
Direct relationship: As one variable increases, the other also increases.Example: Temperature 10, 20, 30 C; dissolved mass 20, 30, 40 g: both rise together.
Inverse relationship: As one variable increases, the other decreases.Example: Pressure 100, 200, 400 kPa; gas volume 4, 2, 1 L: volume falls as pressure rises.
Interpolation: Estimating a value between two measured data points by assuming the trend continues smoothly between them.Example: Dissolved mass is 30 g at 20 C and 40 g at 30 C, so at 25 C it is about 35 g.
Extrapolation: Estimating a value beyond the range of measured data by extending the trend; less certain than interpolation.Example: If the 10 g per 10 C pattern continues, at 40 C about 50 g would dissolve.
Reading a legend or key: Match each symbol, line style or colour to the condition it represents before reading values.Example: Solid line = Species A, dashed line = Species B; at day 6 the dashed line is higher, so B is ahead.
Graphs with two y-axes: Each curve belongs to one y-axis, usually marked by side or label; read every curve against its own axis.Example: Left axis temperature (C), right axis rainfall (mm): the rainfall bars use the right-hand scale.
Nonlinear trends and plateaus: A trend may rise quickly, then level off; describe each region of the graph separately.Example: Enzyme rate 2, 4, 6, 6.5, 6.6 units as substrate rises 1 to 5 mM: rate levels off near 6.6.

Data interpretation and calculation

Rate from a graph: The slope of a straight-line section: change in y divided by change in x, with units.Example: Distance 0, 6, 12 m at times 0, 2, 4 s: 6 m per 2 s = 3 m/s.
Percent change in data: (new - old)/old x 100 compares growth across trials or groups.Example: Bacteria count from 40 to 50 thousand: 25% increase.
Proportional relationship: y = kx: doubling x doubles y, and the graph is a straight line through the origin.Example: Spring stretch 2, 4, 6 cm for 1, 2, 3 N: stretch doubles when force doubles.
Density and floating: Density = mass/volume; an object floats in a liquid if its density is less than the liquid's.Example: 30 g in 12 mL: 2.5 g/mL, so it sinks in water (1.0 g/mL).
Anomalous result: A data point far from the pattern of the others, often from measurement error; it should be checked or repeated, not silently removed.Example: Times 4.1, 4.2, 6.8, 4.3 s: the 6.8 s trial is anomalous.
Error bars: Lines on a data point showing uncertainty or spread; heavy overlap between two such ranges suggests the difference may not be meaningful.Example: Group A 12 plus or minus 3 and Group B 13 plus or minus 3 overlap a lot.
Precision versus accuracy: Precise measurements agree closely with each other; accurate ones are close to the true value.Example: Readings 9.1, 9.2, 9.1 g of a 10.0 g mass are precise but not accurate.
Unit conversions in passages: Convert to matching units before comparing or calculating; watch prefixes such as milli and kilo.Example: 1500 mL = 1.5 L, so a 1.5 L sample and a 1500 mL sample are the same volume.
Scatterplot: A graph of paired measurements as individual points, used to see whether two quantities are related and how strongly.Example: Points for 8 lakes, phosphate (x) vs algae (y), cluster around an upward line: a positive association.
Trend line: A straight line drawn through the middle of scattered points to summarize the pattern and make rough predictions.Example: A line through (0, 2) and (10, 7) has slope 0.5, so at x = 4 it predicts y = 4.
Comparing groups with averages: Compare the mean of each condition rather than single trials, and check whether the gap is large relative to the trial-to-trial spread.Example: Light: 14, 15, 13 cm (mean 14). Shade: 8, 9, 7 cm (mean 8). The 6 cm gap is far larger than the spread.

Evaluating models and claims

Correlation versus causation: Two variables changing together does not prove one causes the other; a third factor or chance may explain it.Example: Ice cream sales and sunburns both rise in July because of sunny weather.
Conflicting viewpoints passage: A passage presenting two or more explanations for the same phenomenon; questions ask what each one claims and which evidence fits each.Example: Scientist 1: craters formed by impacts. Scientist 2: by volcanoes. Each predicts different rock types.
Claim, evidence and reasoning: A claim is a conclusion; evidence is data supporting it; reasoning explains why the evidence supports the claim.Example: Claim: shade slows growth. Evidence: 8 cm vs 14 cm. Reasoning: less light means less photosynthesis.
Evidence that weakens a claim: A result that contradicts a prediction, or gives an alternative explanation, makes the claim less likely.Example: If plants grow equally in shade and sun, the claim that light limits growth is weakened.
Predicting from a model: Apply the model's stated rule to a new case and check that the case stays within the conditions the model covers.Example: Model: each 100 m of altitude lowers air temperature about 0.6 C. At 500 m higher, about 3 C cooler.
Engineering constraints and trade-offs: Limits a design must respect (cost, mass, materials) and the compromise between competing goals.Example: A lighter bridge model uses less material but holds 20% less load.

Test strategy

Figure-first approach: Read the question, locate the matching table or figure, and answer from the data before rereading passage text.Example: A question about Table 2 at 30 C: go to Table 2, the 30 C row, and read the value.
Pacing on the science section: With about one minute per question, skip a stuck item, mark it and return later.Example: Ten minutes left with 12 questions remaining: answer the quick data look-ups first.