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A study may discuss exercise, AI use, stress, treatment, or lived experience while examining something much more specific. Identify exactly what was observed, delivered, or explored.
Read Guide →A study's design is defined by how participants, exposures, interventions, outcomes, and time were organized, not simply by the methodological label printed in the paper.
Read Guide →The prose surrounding a statistical result can influence how strong, important, or certain it seems. Learn how to inspect the numbers first and then evaluate whether the authors’ narrative accurately represents them.
Read Guide →A favorable subgroup finding can attract attention even when the study’s primary result is weak or inconclusive. Learn how to identify that shift and evaluate whether the subgroup effect is genuinely supported.
Read Guide →Critical appraisal is more than finding limitations. Learn how to judge whether a study asked a worthwhile question, used appropriate methods, produced credible results, and made conclusions the evidence can actually support.
Read Guide →Yes. Publication in a prestigious journal can be a useful signal about selectivity and scholarly attention, but it does not guarantee that an individual study is methodologically strong or that its conclusions are justified.
Read Guide →Every study has limitations, but not every limitation invalidates its findings. The key question is whether a methodological problem merely weakens or narrows an inference, or prevents the study from supporting its central claim at all.
Read Guide →A well-executed study can still answer the wrong question. Evaluate whether the design produces the kind of evidence required by the research question before deciding how much confidence to place in its conclusions.
Read Guide →An appropriate sample is not simply a large one. Judge whether the people, cases, records, or other units studied are suitable for the research question, how they were selected, who may be missing, and how far the resulting evidence can reasonably be generalized.
Read Guide →A causal claim says more than two variables are associated. It says changing one would change the other. Evaluate whether the study establishes temporal order, provides a credible comparison, addresses confounding and selection, measures the relevant variables adequately, and rules out plausible alternative explanations.
Read Guide →A small sample may reduce statistical power or precision, but sample size cannot be judged in isolation. What matters is whether the sample is adequate for the research question, design, analysis, and claims being made.
Read Guide →A large sample can improve statistical power and precision, but size alone does not make evidence trustworthy. Sampling, measurement, design, analysis, and the claims being made still determine what the data can support.
Read Guide →Good mixed-methods research is more than a quantitative study and a qualitative study placed in the same paper. Each component should be rigorous, but the crucial question is whether combining them produces an integrated understanding that neither could provide as effectively alone.
Read Guide →Discovering serious problems in an important paper does not automatically tell you what to do next. Verify the problem, determine which claims it affects, reassess the surrounding evidence, and revise your own conclusions in proportion to the damage.
Read Guide →Clear writing can make a study easier to understand, but it does not make the underlying methods stronger. Learn how to evaluate methodological quality without being overly persuaded by polished presentation.
Read Guide →A study may have been conducted rigorously yet reported too incompletely for readers to verify that rigor. Learn why poor reporting creates uncertainty rather than automatically proving poor methodology.
Read Guide →Not every research question requires a sample that mirrors a wider population. Whether representativeness matters depends on what researchers are trying to estimate, explain, test, or generalize.
Read Guide →Self-report is sometimes the most appropriate way to measure a construct, particularly when the construct concerns experiences only the participant can report. Its quality depends on what is being measured and how the questions are designed and administered.
Read Guide →Critical appraisal usually does not require reproducing every statistical calculation in a paper. The more important task is judging whether the methods, assumptions, reported estimates, uncertainty, and conclusions make sense together.
Read Guide →P-values, effect sizes, and confidence intervals answer different questions. Good interpretation usually emphasizes the estimated effect and its uncertainty while using the P-value as supplementary evidence rather than the entire conclusion.
Read Guide →A statistically significant result can be too small to matter in practice. Statistical significance concerns evidence relative to a statistical model, while importance depends on effect magnitude, uncertainty, context, and consequences.
Read Guide →You do not need to master every statistical method before you can critically appraise a paper. Evaluate what you can verify, identify exactly what remains uncertain, and seek appropriate statistical expertise when the unresolved method is central to the conclusion.
Read Guide →Not every paper requires consultation with a statistician. Seek appropriate statistical expertise when unresolved analytical questions are central to the study's conclusions, difficult to evaluate confidently, or consequential for how you will use the evidence.
Read Guide →Systematic qualitative analysis does not require one universal coding procedure. It requires a coherent, transparent process that shows how researchers moved from raw data to defensible interpretations.
Read Guide →Quantitative and qualitative data can be analyzed separately within a legitimate mixed methods study. The crucial question is whether those analyses are eventually connected in a way that matters.
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