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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 →Peer review is an important form of scholarly scrutiny, not a guarantee that a published study is correct or reliable. A peer-reviewed paper still needs to be evaluated on its design, methods, analysis, reporting, and the strength of the evidence behind its conclusions.
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 →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 →Every study has limitations, but not every limitation deserves the same weight. Learn how to judge whether a weakness merely narrows a finding, reduces confidence in it, or seriously undermines the conclusion.
Read Guide →A systematic review can summarize an entire body of research, but its conclusions are only as credible as the methods used to find, select, evaluate, and synthesize the evidence. A meta-analysis adds statistical synthesis, not an automatic guarantee of stronger evidence.
Read Guide →A paper can change status after publication through a correction, expression of concern, or retraction. Before relying on an important study, check the publisher's current record and relevant scholarly databases rather than assuming the version you found reflects its present status.
Read Guide →A retracted paper should generally not be used as ordinary valid evidence for the claim that led to its retraction. It may still belong in a literature review when the retraction itself, its historical influence, or the consequences of unreliable research are relevant to the review.
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 →When key methodological information is unavailable, neither complete trust nor automatic rejection is usually justified. Learn how to calibrate confidence according to what remains unknown and why it matters.
Read Guide →More observations do not automatically mean better population evidence. A smaller sample selected through a stronger design can sometimes provide more trustworthy information than a huge convenience sample.
Read Guide →Measurement problems should not automatically disqualify a study, nor should they be treated as minor footnotes. Their importance depends on which variables are affected, how severe the problem is, and how much the study’s conclusions depend on those measurements.
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 →Being unable to reproduce a statistical result does not automatically make it wrong. Judge why reproduction failed, what evidence remains independently assessable, and how much your conclusion depends on the unverified analysis.
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 →Selective reporting occurs when which results are reported, emphasized, or fully presented depends on what the analyses found. Look beyond the published P-values by comparing the paper with protocols, registrations, analysis plans, and other study records.
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 →A convincing qualitative study does more than present interesting themes and quotations. Its claims should be supported by an appropriate design, relevant participants, sufficiently rich data, systematic analysis, reflexivity, and a transparent connection between evidence and interpretation.
Read Guide →The right qualitative participants are not necessarily representative of a population. They are people whose experiences, positions, or knowledge can meaningfully illuminate the specific research question.
Read Guide →There is no universal number of participants that makes a qualitative sample adequate. Sample adequacy depends on how much relevant information the sample provides for the research question, methodology, and intended analysis.
Read Guide →Qualitative studies usually do not aim to produce statistically representative samples. Their sampling should instead be judged against the research question, the cases selected, the depth of inquiry, and the claims the researchers make.
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.
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