Science: conflicting viewpoints and evidence that separates models
| English | 中文 | Pinyin · 拼音 |
|---|---|---|
| discriminating test/dɪˈskrɪmɪneɪtɪŋ test/ | 鉴别性检验 | jiàn bié xìng jiǎn yàn |
| model assumption/ˈmɒdl əˈsʌmpʃn/ | 模型假设 | mó xíng jiǎ shè |
A decision before an answer
- Evidence predicted by both models cannot, by itself, decide between them.
- Your goal: Represent each model’s assumptions and predictions separately.
Read the relationship
- Conflicting Viewpoints passages present alternative explanations of the same observation. Label each model’s mechanism, assumptions and predicted behaviour before answering. A model may agree about the observation while disagreeing about the cause. Keep track of whether the stem asks what a model predicts or what the observed data actually establish.
- Select observations that discriminate between competing explanations.
Which comparison most directly separates A and B?
Temperature differs while runtime is fixed, isolating the competing dependence.
Use the defining rule
- A useful discriminating test places models where their predictions differ. Evidence consistent with both supports neither uniquely. Contradiction can weaken a model on its stated assumptions; it need not prove that a competing model is the only possible explanation. Identify conditions that could invalidate the comparison, including an unstated change in measurement method.
- Evaluate a model or design without claiming more than the evidence warrants.
Both models predict dimming after use. Observing only dimming:
A shared prediction is not discriminating evidence.
Check the conditions
- When extending a model, apply its stated relationship consistently to the new situation. Do not import a familiar scientific law if the passage defines a different hypothetical mechanism. Compare predictions numerically where possible, and mark the assumptions needed for a causal inference. Results can lead to a revised model rather than a simple winning viewpoint.
- Evaluate a model or design without claiming more than the evidence warrants.
Original hypothetical study: a device’s indicator dims during repeated use. Model A says dimming depends only on elapsed running time: after 10 minutes it predicts the same brightness at all room temperatures. Model B says dimming is caused by an internal temperature rise: after 10 minutes it predicts lower brightness in a warmer room, provided the starting devices are identical. Test matched devices at 15°C and 30°C for ten minutes, controlling initial charge and measurement method. Equal brightness is consistent with A but does not decisively reject B unless temperature differences and sensitivity are verified. Much lower brightness at 30°C conflicts with A’s “time only” assumption and supports B’s predicted direction. A cooling modification should also be tested for cost and power consumption before recommending it.
In the model comparison, runtime should be held ____ when room temperature is varied.
Otherwise runtime and temperature effects could be confounded.
Apply the task format
- Engineering design thinking weighs evidence against a target and considers a modification’s trade-offs. Explain why a change might address the observed failure and what new test would verify it. A design recommendation should use the passage’s criteria and acknowledge competing effects, rather than assume that the strongest material, highest speed or lowest price is always optimal.
- Evaluate a model or design without claiming more than the evidence warrants.
A directionally supportive result is not unique proof. Check that the competing predictions truly differ under the test conditions.
Which answer fits this case?
Represent each model’s assumptions and predictions separately
One observation agreeing with a model proves that no other explanation is possible.
Other models may share the prediction or fit the evidence.
Keep the distinctions
- discriminating test 鉴别性检验 — A test under conditions where competing models predict different outcomes.
- model assumption 模型假设 — A condition or relationship taken as given when deriving predictions.
- Represent each model’s assumptions and predictions separately.
- Select observations that discriminate between competing explanations.
- Evaluate a model or design without claiming more than the evidence warrants.
Match each term with its precise meaning in this lesson.
Keep the distinctions stated in the teaching example.
Put this lesson’s reasoning or event sequence in order.
The order follows the stated process; check each stage before the next.