Evaluating Models & Explanations - ACT Science

Higher quality data improves accuracy. Better data inputs lead to more reliable model outputs and predictions.

Results can be sensitive to initial conditions. Small input changes can dramatically alter simulation outcomes.

A model that accounts for changes over time. Captures how systems evolve and change through time.

The mean becomes more reliable and precision increases. Multiple trials average out random variations.

A constant positive offset (zeroing error). Consistent positive offset indicates calibration error.

$Y$ depends on $X$ with a threshold effect. Data shows response only above a critical value.

The measured outcome that responds to the independent variable. The dependent variable responds to changes in the independent variable.

Model iteration. Scientists continuously refine models as new data becomes available.

Models support, but do not prove, theories. Models provide evidence for theories but cannot definitively prove them.

Lack of detail and accuracy. Oversimplified models miss important variables affecting real-world outcomes.

A scale model of the solar system. Physical models use tangible objects to represent real-world structures.

A simulation is a dynamic model execution. Simulations actively run models through time or different conditions.

Consistency with evidence. Scientific explanations must align with observed data and experimental results.

Focus on detailing phenomena. Descriptive models emphasize accurate representation of observed phenomena.

Random error. Repetition averages out random fluctuations.

Evaluate how well it explains observed phenomena. Good models should account for and predict observed patterns effectively.

To ensure the model is scrutinized by other experts. Independent evaluation identifies flaws and validates model quality.

New data that contradicts the model's predictions. Models must adapt when evidence shows their predictions are wrong.

Results can be sensitive to initial conditions. Small input changes can dramatically alter simulation outcomes.

The range of conditions under which it is applicable. Defines the boundaries where the model produces reliable results.

A model based on observed and measured data. Built directly from experimental observations and measurements.

It produces the same output from the same input. No randomness - identical inputs always yield identical outputs.

To serve as a baseline for comparison. Controls isolate the effect being studied from other variables.

Excellent fit to training data but poor generalization. Model memorizes training data instead of learning general patterns.

Unjustified assumptions can lead to inaccuracies. Faulty assumptions propagate errors throughout model predictions.

Increased complexity may reduce ease of use. More complex models are often harder to understand and apply.

Assessing how changes in inputs affect outputs. Tests how robust model predictions are to input variations.

Provides insight without requiring precise measurement. Captures general patterns without requiring exact numerical data.

Incorporates randomness in predictions. Accounts for uncertainty and variability in real-world systems.