Scientific Modeling and Data Interpretation

What is a scientific model?

Types of models: physical, mathematical, conceptual

Purpose and limitations of models

Building models from observations

Testing models with predictions

Revising models based on evidence

Data collection: accuracy and precision

Types of error: systematic and random

Significant figures

Scientific notation in data

Data tables: organization

Graphing data: choosing graph types

Graphing data: scatter plots

Lines of best fit

Linear regression analysis

Interpreting slope and intercept

Non-linear relationships

Linearizing data

Correlation and causation

Uncertainty and error bars

Propagation of uncertainty

Statistical analysis in science

Drawing conclusions from data

Peer review and reproducibility

Communicating scientific findings

Reading scientific literature

Evaluating scientific claims

Models in current research

Interdisciplinary modeling

Unit assessment