ACT® · Science: Evaluating Scientific Arguments and Models with Evidence (8-13 items)
Using New Information to Make a Prediction From a Model (EMI 603)
A model earns its usefulness when it predicts something new. To use it, you find the cause-and-effect rule the scientist states, plug in the new condition, and read off what the rule says should happen.
A model is only worth something once it can speak to a case nobody has measured yet. If it only describes data already collected, it is just a summary. Every passage that gives you a scientist's model is handing you a rule: some cause pushes some effect in some direction, often with a trigger value or a rate attached. Your job is to take a new number the question gives you and run it through that same rule.
To do this, find the exact wording of the cause-and-effect chain. If a model says bleaching risk climbs by a fixed number of percentage points for every degree above a trigger temperature, then a new temperature reading either sits above that trigger, adding risk, or below it, lowering risk. You are not inventing new reasoning. You are extending the scientist's own logic one step further than the passage already took it, using the same direction of cause and effect the scientist described.
This skill sits right next to checking whether a model even applies. A model stated for soils between 10 and 20 percent organic content has nothing reliable to say about a soil at 60 percent, because that value falls outside the range the model was built on. Recognizing that boundary is the flip side of making a prediction: sometimes the right answer is that the model cannot forecast anything trustworthy for the new condition, because it was never tested or stated there.
Key terms
- model
- A scientist's proposed explanation linking a cause to an effect, often with a specific trigger value or rate.
- trigger value
- The specific condition named in a model above or below which an effect is said to occur.
- extrapolation
- Extending a model's logic to a condition beyond the range it was actually stated or tested for.
- stated range
- The set of conditions a model's claim explicitly covers, outside of which the model makes no reliable prediction.
Watch it explained
- How it worksHow do you feed a new condition into a scientist's explanation and read out what it forecasts?
- How it worksWalk me through extending a model's cause-and-effect chain into a situation it never mentioned.
- Worked exampleFind what a model blaming warm water for bleaching forecasts for a reef whose water cooled 2 degrees C.
- Going furtherWhat happens to a model-based forecast when the new condition sits outside the model's stated range?
Check yourself
End of topic.