Evaluating Models & Explanations - ACT Science
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What is a key feature of a good explanatory model?
What is a key feature of a good explanatory model?
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Testability. Good models generate predictions that can be experimentally verified.
Testability. Good models generate predictions that can be experimentally verified.
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What is the relationship between data and model accuracy?
What is the relationship between data and model accuracy?
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Higher quality data improves accuracy. Better data inputs lead to more reliable model outputs and predictions.
Higher quality data improves accuracy. Better data inputs lead to more reliable model outputs and predictions.
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Identify a limitation of using computer simulations.
Identify a limitation of using computer simulations.
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Results can be sensitive to initial conditions. Small input changes can dramatically alter simulation outcomes.
Results can be sensitive to initial conditions. Small input changes can dramatically alter simulation outcomes.
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What is a dynamic model?
What is a dynamic model?
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A model that accounts for changes over time. Captures how systems evolve and change through time.
A model that accounts for changes over time. Captures how systems evolve and change through time.
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What is the correct interpretation if repeated trials reduce random error in a model test?
What is the correct interpretation if repeated trials reduce random error in a model test?
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The mean becomes more reliable and precision increases. Multiple trials average out random variations.
The mean becomes more reliable and precision increases. Multiple trials average out random variations.
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Identify the systematic error: A scale reads $+2$ g for every object, including a $0$ g standard.
Identify the systematic error: A scale reads $+2$ g for every object, including a $0$ g standard.
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A constant positive offset (zeroing error). Consistent positive offset indicates calibration error.
A constant positive offset (zeroing error). Consistent positive offset indicates calibration error.
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Identify the best-supported statement: Data show $Y$ changes only when $X$ exceeds a threshold.
Identify the best-supported statement: Data show $Y$ changes only when $X$ exceeds a threshold.
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$Y$ depends on $X$ with a threshold effect. Data shows response only above a critical value.
$Y$ depends on $X$ with a threshold effect. Data shows response only above a critical value.
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What is a dependent variable when evaluating a model using an experiment?
What is a dependent variable when evaluating a model using an experiment?
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The measured outcome that responds to the independent variable. The dependent variable responds to changes in the independent variable.
The measured outcome that responds to the independent variable. The dependent variable responds to changes in the independent variable.
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Which term describes the refinement of a model as new data emerges?
Which term describes the refinement of a model as new data emerges?
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Model iteration. Scientists continuously refine models as new data becomes available.
Model iteration. Scientists continuously refine models as new data becomes available.
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Identify the error in this statement: 'Models prove theories.'
Identify the error in this statement: 'Models prove theories.'
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Models support, but do not prove, theories. Models provide evidence for theories but cannot definitively prove them.
Models support, but do not prove, theories. Models provide evidence for theories but cannot definitively prove them.
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Determine the primary weakness of a simplistic model.
Determine the primary weakness of a simplistic model.
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Lack of detail and accuracy. Oversimplified models miss important variables affecting real-world outcomes.
Lack of detail and accuracy. Oversimplified models miss important variables affecting real-world outcomes.
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What is an example of a physical model?
What is an example of a physical model?
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A scale model of the solar system. Physical models use tangible objects to represent real-world structures.
A scale model of the solar system. Physical models use tangible objects to represent real-world structures.
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What is the difference between a model and a simulation?
What is the difference between a model and a simulation?
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A simulation is a dynamic model execution. Simulations actively run models through time or different conditions.
A simulation is a dynamic model execution. Simulations actively run models through time or different conditions.
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Which factor is most crucial when evaluating a scientific explanation?
Which factor is most crucial when evaluating a scientific explanation?
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Consistency with evidence. Scientific explanations must align with observed data and experimental results.
Consistency with evidence. Scientific explanations must align with observed data and experimental results.
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What distinguishes a descriptive model from other models?
What distinguishes a descriptive model from other models?
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Focus on detailing phenomena. Descriptive models emphasize accurate representation of observed phenomena.
Focus on detailing phenomena. Descriptive models emphasize accurate representation of observed phenomena.
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Which type of error is reduced by repeating measurements many times: random or systematic?
Which type of error is reduced by repeating measurements many times: random or systematic?
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Random error. Repetition averages out random fluctuations.
Random error. Repetition averages out random fluctuations.
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Identify a way to assess a model's explanatory power.
Identify a way to assess a model's explanatory power.
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Evaluate how well it explains observed phenomena. Good models should account for and predict observed patterns effectively.
Evaluate how well it explains observed phenomena. Good models should account for and predict observed patterns effectively.
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What is the role of peer review in model evaluation?
What is the role of peer review in model evaluation?
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To ensure the model is scrutinized by other experts. Independent evaluation identifies flaws and validates model quality.
To ensure the model is scrutinized by other experts. Independent evaluation identifies flaws and validates model quality.
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Which factor can lead to a model's revision?
Which factor can lead to a model's revision?
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New data that contradicts the model's predictions. Models must adapt when evidence shows their predictions are wrong.
New data that contradicts the model's predictions. Models must adapt when evidence shows their predictions are wrong.
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Identify a limitation of using computer simulations.
Identify a limitation of using computer simulations.
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Results can be sensitive to initial conditions. Small input changes can dramatically alter simulation outcomes.
Results can be sensitive to initial conditions. Small input changes can dramatically alter simulation outcomes.
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What is a model's scope?
What is a model's scope?
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The range of conditions under which it is applicable. Defines the boundaries where the model produces reliable results.
The range of conditions under which it is applicable. Defines the boundaries where the model produces reliable results.
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What is an empirical model?
What is an empirical model?
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A model based on observed and measured data. Built directly from experimental observations and measurements.
A model based on observed and measured data. Built directly from experimental observations and measurements.
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What does it mean if a model is deterministic?
What does it mean if a model is deterministic?
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It produces the same output from the same input. No randomness - identical inputs always yield identical outputs.
It produces the same output from the same input. No randomness - identical inputs always yield identical outputs.
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Identify the primary purpose of a control group in experiments.
Identify the primary purpose of a control group in experiments.
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To serve as a baseline for comparison. Controls isolate the effect being studied from other variables.
To serve as a baseline for comparison. Controls isolate the effect being studied from other variables.
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Identify a sign of a model's overfitting.
Identify a sign of a model's overfitting.
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Excellent fit to training data but poor generalization. Model memorizes training data instead of learning general patterns.
Excellent fit to training data but poor generalization. Model memorizes training data instead of learning general patterns.
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How can model assumptions affect outcomes?
How can model assumptions affect outcomes?
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Unjustified assumptions can lead to inaccuracies. Faulty assumptions propagate errors throughout model predictions.
Unjustified assumptions can lead to inaccuracies. Faulty assumptions propagate errors throughout model predictions.
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What is the impact of model complexity on usability?
What is the impact of model complexity on usability?
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Increased complexity may reduce ease of use. More complex models are often harder to understand and apply.
Increased complexity may reduce ease of use. More complex models are often harder to understand and apply.
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What is sensitivity analysis in model evaluation?
What is sensitivity analysis in model evaluation?
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Assessing how changes in inputs affect outputs. Tests how robust model predictions are to input variations.
Assessing how changes in inputs affect outputs. Tests how robust model predictions are to input variations.
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Identify a strength of using a qualitative model.
Identify a strength of using a qualitative model.
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Provides insight without requiring precise measurement. Captures general patterns without requiring exact numerical data.
Provides insight without requiring precise measurement. Captures general patterns without requiring exact numerical data.
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What is a stochastic model?
What is a stochastic model?
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Incorporates randomness in predictions. Accounts for uncertainty and variability in real-world systems.
Incorporates randomness in predictions. Accounts for uncertainty and variability in real-world systems.
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