Reason About Scientific Principles, Theories, and Models - MCAT Chemical and Physical Foundations of Biological Systems

Theory explains why; law describes what happens (often mathematically). Scientific theories provide explanatory mechanisms for phenomena, whereas laws are generalized descriptions of consistent patterns, often expressed mathematically.

A simplified representation used to explain and predict observations. Scientific models abstract complex realities into manageable forms to facilitate understanding and forecasting of natural behaviors.

Results generalize to other populations, settings, or conditions. External validity assesses applicability beyond the study context, enhancing the broader relevance of results.

The measured response that depends on the independent variable. Dependent variables capture the effects of experimental changes, allowing quantification of relationships in studies.

Provide a baseline for comparison to isolate the independent variable’s effect. Control conditions minimize extraneous variables, ensuring observed effects stem from the independent variable alone.

$H_0$: no difference or no effect between conditions. The null hypothesis assumes no relationship or impact, serving as the default position to be tested against evidence.

It is falsifiable by an observation or experiment. Falsifiability ensures hypotheses can be tested and potentially disproven, a cornerstone of the scientific method distinguishing it from pseudoscience.

Systematic error. Systematic errors introduce consistent bias, affecting measurement trueness by directional deviation.

$H_A$: a difference exists or an effect is present. The alternative hypothesis posits a specific relationship or effect, opposing the null and requiring statistical support to accept.

It is close to the true or accepted value. Accuracy reflects how well measurements align with the actual quantity, independent of measurement consistency.

It yields tightly clustered repeated measurements. Precision indicates reproducibility of measurements, showing low variability regardless of proximity to the true value.

Random error. Random errors arise from unpredictable fluctuations, broadening the distribution of measurements without bias.

The variable intentionally manipulated by the experimenter. Independent variables are controlled to test their influence on outcomes, enabling causal inference in experimental designs.

It varies with the independent variable and also affects the outcome. Confounders obscure true causal links by correlating with both independent and dependent variables, leading to spurious associations.

A third variable or reverse causation can produce the association. Correlations may result from confounding factors or bidirectional influences, necessitating experimental controls for causal claims.

A causal account of how a process produces an outcome. Mechanistic explanations detail underlying processes and interactions, going beyond mere associations to elucidate causality.

Independent researchers can obtain consistent results under similar methods. Reproducibility confirms reliability through replication, strengthening scientific validity and trust in findings.

Observed effects are attributable to the manipulation, not confounders. Internal validity ensures experimental manipulations cause observed effects, free from alternative explanations like biases.

Predictions are reliable only when the assumptions are approximately met. Model assumptions define the conditions under which predictions hold, limiting validity when violated.

Prefer the simplest model that adequately explains the data. Parsimony favors minimal complexity to avoid overfitting, balancing explanatory power with simplicity in model selection.

Revise or replace the model; do not discard the data without justification. Empirical evidence takes precedence over theoretical predictions, prompting model refinement to align with observations.

Reject $H_0$; evidence supports an effect under the tested conditions. A p-value below the significance level indicates sufficient evidence against the null, supporting the alternative hypothesis in the trial context.

Fail to reject $H_0$; insufficient evidence for an effect. A p-value exceeding alpha prevents rejection of the null, implying data do not provide strong evidence for the hypothesized effect.