Science 25 min

Evaluating Models, Inferences & Results

Judging hypotheses and competing viewpoints against the data.

This is the “who’s right?” skill. You’ll weigh hypotheses and viewpoints against evidence — and the key is judging them by the passage, not by what you think is true.

What it tests

Evaluating Models, Inferences & Experimental Results questions ask you to judge explanations: which hypothesis the data supports, what a model predicts, where competing viewpoints agree or disagree, and whether a conclusion actually follows from the results.

How it shows up

The Science test is 40 questions in 40 minutes — one minute per question. On the enhanced ACT it is optional: it isn’t part of your composite (English + Math + Reading), but it gets its own score and counts toward the STEM score, and many colleges still like to see it. Passages show data (tables and graphs), describe experiments, or present competing viewpoints.
Evaluating Models is a mid-sized Science category. It shows up most in the Conflicting Viewpoints passage (two or more scientists or students explaining the same thing) and at the end of experiment passages.
Typical stems: “Which finding would most strengthen Scientist 2’s view?”, “Both students would agree that…”, “Based on Hypothesis 1, which result would be expected?”, and “Is this conclusion supported? Yes, because… / No, because…”.

Key rules & concepts

The content you actually need to know.

Conflicting viewpoints

Read the introduction first — it states the facts everyone agrees on.
Summarize each viewpoint in one line. Note the specific CAUSE or MECHANISM each one proposes.
Look for overlap (what they both accept) and the exact point where they disagree.

Strengthen or weaken

A finding STRENGTHENS a viewpoint if it’s what that viewpoint predicts.
A finding WEAKENS a viewpoint if it contradicts that viewpoint’s cause or prediction.
A finding that fits both views equally doesn’t help decide between them.

Predictions from a model

Apply the model’s rule to the new situation exactly as stated — even if you think the model is wrong.
If a model says “X causes Y,” it predicts more Y when X increases.

Evaluating conclusions

A conclusion must match the data AND stay within the conditions tested.
For “Yes, because… / No, because…” questions: decide yes/no from the data, then pick the reason that states the correct evidence.

Strategy & traps

Step by step

1Read the intro and each viewpoint; write a one-line summary of each.
2For new-evidence questions, ask “Which viewpoint predicted this?”
3For agree/disagree questions, check that both (or all) viewpoints actually say it.
4For Yes/No questions, check the data first, then match the reason.

Real-life bias

Don’t pick the viewpoint you believe is true — judge them by the passage.

True but irrelevant reason

A reason can quote real data and still not be the evidence that matters.

Mixing up who said what

Keep your one-line summaries handy and check them for every question.

Board Buddy tip: Judge each viewpoint by the passage, not by what you think is true in real life.

Worked examples

Try each one first, then reveal the solution.

The Frogs of Miller Pond

1Over ten years, the number of frogs living in Miller Pond fell by 70%. Two students proposed explanations.

2Student 1: The decline was caused by a fungus that infects frog skin. Infected frogs have trouble absorbing water and salts through their skin, and many die. The fungus spreads most easily in cool, wet years.

3Student 2: The decline was caused by fish that were added to the pond ten years ago. The fish eat frog eggs and tadpoles, so fewer young frogs survive to become adults. Adult frogs are not harmed by the fish.

Example 1

Which finding, if true, would most strengthen Student 2’s explanation?

AMany adult frogs in Miller Pond are infected with the skin fungus.
BIn a nearby pond with no fish, the frog population stayed about the same over the same ten years.
CThe weather near Miller Pond was unusually cool and wet over the ten years.
DAdult frogs in Miller Pond live longer than frogs in other ponds.

Example 2

Students 1 and 2 would most likely agree that:

Athe number of frogs in Miller Pond decreased.
Bfish eat adult frogs.
Cthe fungus spreads most easily in warm years.
Dpollution caused the decline.

Example 3

If Student 1’s explanation is correct, frog deaths would most likely have been highest in years that were:

Awarm and dry.
Bwarm and wet.
Ccool and dry.
Dcool and wet.

Practice

Pick an answer for instant feedback. Answers lock once chosen.

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The Shrinking Lake

1The average water level of Lake Verran dropped 1.8 m between 2010 and 2020. Three scientists offered explanations.

2Scientist 1: Warmer summers increased evaporation. As summer temperatures around the lake rose, more water evaporated from the lake’s surface each year.

3Scientist 2: Farms near the lake began pumping more water for irrigation. The drop was caused mainly by these withdrawals, not by the weather.

4Scientist 3: Less snow fell in the mountains that feed the lake’s rivers, so less meltwater reached the lake each spring. Evaporation and irrigation changed too little over the decade to explain the drop.

Table 1: Lake Verran measurements

Year	Average summer temperature (°C)	Irrigation withdrawals (million m³)	Spring river inflow (million m³)
2010	24.1	12	410
2015	24.3	13	330
2020	24.2	12	260

Question 1

The data in Table 1 best support the explanation of which scientist?

Question 2

Scientist 1 would most likely predict that, from 2010 to 2020, the average summer temperature near the lake:

Question 3

Which statement would ALL three scientists most likely agree with?

Question 4

Suppose a later study found that the irrigation numbers in Table 1 counted only farms on the north shore, and that total withdrawals from the lake actually tripled between 2010 and 2020. This finding would most directly strengthen the explanation of:

Question 5

Are the data for 2015 consistent with Scientist 3’s explanation?