Nature and meaning of science

A scientific explanation must allow the possibility of an opposing result. Treat falsifiability as a key criterion.

Scientific language should match the strength of evidence. Identify cautious conclusion wording.

Results may be consistent in artificial settings but application to real life may be limited. Distinguish the two qualities.

The direct answer is A: define clear and measurable criteria. An operational definition explains exactly how a scientist will recognize or measure a variable. For example, instead of saying that a plant is healthy, the study might define health as a particular height, number of leaves, or mass measured in a stated way. If observers use different meanings, their records cannot be compared reliably. Option A is correct because pre-decided, observable, measurable rules reduce observer differences and improve consistency. Option B is wrong: using fewer observers does not repair an unclear definition and may reduce useful checking. Option C is wrong because an average can summarize numbers but cannot decide what was measured or remove inconsistent meanings. Option D is wrong and scientifically unacceptable because deciding the outcome afterward invites bias. The correct procedure is to define the variable and its measurement rule before collecting results. Memory cue: first define how to measure, then measure.

Testing in different settings examines external validity. Look for diverse testing when judging generalisation.

Precision may clarify the existence of an effect but not its importance. Distinguish effect size from usefulness.

Small studies may be more affected by chance and selective publication. Examine study size and publication bias.

A claim of error should be based on evidence and documentation. Choose transparent error analysis.

Within ethical limits observations models and natural comparisons can be useful. Identify safe alternative methods.

Units clarify the magnitude and comparability of measurements. Treat units as essential with numerical values.

Expert review is important but transparent data strengthen independent checking. Distinguish authority from evidence.

An unmeasurable variable may make a model arbitrary and untestable. Examine testability along with complexity.

Different levels of attention may be confused with the treatment effect. Compare contact and care across groups.

When evidence is balanced uncertainty is scientifically appropriate. Match confidence to the evidence.

Predefined exclusion criteria improve transparency and objectivity. Recognise advance planning.

An instrument-specific result may arise from a unique error or sensitivity. Seek verification with alternative instruments.

A scientific study may support a hypothesis but usually does not provide absolute final proof. Choose cautious language.

Changing groups may blur the relationship between treatment and outcome. Examine adherence to group assignment.

Simulation is useful but its assumptions and predictions should be tested with real data. Understand the link between models and evidence.

Benefit and safety are separate outcomes and both require measurement. Identify incomplete outcome reporting.

In scientific conclusions, the quality of studies matters more than their number alone. Strong studies use appropriate methods, adequate samples, controlled conditions, reliable data, and steps to reduce bias. Therefore, even if they are fewer, stronger studies should be given priority. A larger number of studies is not enough if those studies have weak designs or are biased. Exam tip: When judging evidence, check the study method and reliability first.

A better theory retains earlier successful explanations while accounting for new evidence. Recognise scientific refinement.

Uncertainty clarifies evidence limits and conclusion strength. Treat it as scientific honesty.

Scientific evidence describes outcomes while decisions about proper use may also involve ethical and social values. Distinguish facts from values.

Science includes knowledge as well as the processes of creating testing limiting and improving it. Recognise all dimensions of science.