Nature and meaning of science

The same error can produce a similar wrong conclusion across many studies. Check independence as well as quantity of evidence.

Without controlled intervention it is difficult to rule out other causes. Look for controlled experiments when judging causation.

The model may memorise specific patterns in old data without generalising. Remember the importance of testing on new data.

Consistent readings show precision while constant distance from the true value shows inaccuracy. Distinguish the two.

A claim adjusted to fit every outcome cannot be effectively tested. Identify clear falsifiable hypotheses.

Direct answer: Option B, random assignment of participants. For a fair causal comparison, the treatment and control groups should be similar before the treatment begins. Random assignment gives each participant a fair chance of entering either group. With enough participants, existing differences such as ability, age, health, or motivation are more likely to be balanced between groups. Step by step: recruit participants; randomly assign them to treatment or control; apply the treatment only to the treatment group; compare outcomes; attribute differences more reasonably to the treatment, while considering other sources of error. Option B is correct because it strengthens causal inference. Option A is wrong because changing groups after seeing results can introduce manipulation and does not repair the original comparison. Option C is wrong because increasing only one group can create unequal size and does not remove starting differences. Option D is wrong because removing the control group removes the comparison needed to judge the treatment. Exam cue: unequal starting groups suggest random assignment.

Knowledge of group assignment can influence expectations and behaviour. Link blinding with expectation bias.

A representative sample supports external validity while non-random assignment may weaken causal inference. Distinguish sampling from assignment.

When different methods reach the same conclusion the chance of a shared methodological error decreases. Recognise converging evidence.

A large percentage from a small baseline may represent a small actual difference. Examine both relative and absolute change.

Selecting the primary outcome after viewing data can increase selective reporting. Look for predefined outcome measures.

Financial interests do not automatically invalidate results but independent review is needed. Examine transparency and evidence.

Without clear measurement criteria the explanation is difficult to test empirically. Look for operational definitions.

A fine scale may improve precision while systematic error reduces accuracy. Distinguish the two.

Repetition within the same setup shows repeatability but not independent reproduction. Distinguish the terms.

An overall average may hide important differences among groups. Examine distributions and subgroups.

Expert agreement may matter but it should be grounded in evidence. Examine both consensus and evidence.

Making a universal claim from limited conditions goes beyond the evidence. Examine the scope of a claim.

Random assignment reduces group differences but assessor expectations may still affect measurement. Look for blinded assessment.

A model's predictive usefulness may differ from the literal truth of its assumptions. Distinguish usefulness from truth.

Differences in measurement timing may be confused with treatment effects. Keep measurement conditions comparable.

Negative results reveal failed attempts and the real limits of an effect. Value the complete evidence picture.

With many analyses some unusual findings may occur by chance. Look for predefined subgroups and independent confirmation.

The impact of one study should be judged by its quality and the wider evidence. Examine the total weight of evidence.

People without internet access were excluded so representativeness is limited. Examine sample coverage.