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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.
Read Guide →Mixed methods samples do not always need to contain the same participants, but their relationship should fit the study's purpose. Learn how to evaluate whether that connection is methodologically defensible.
Read Guide →Mixed-methods integration can produce an insight that is not available from either component alone. But a genuinely new integrated conclusion still has to follow defensibly from the relationship between the underlying evidence.
Read Guide →Agreement between quantitative and qualitative findings can strengthen an interpretation, but not automatically. Its value depends on evidence quality, independence, construct alignment, and what exactly has converged.
Read Guide →More methods do not automatically produce more understanding. Mixed-methods complexity earns its place when integrating quantitative and qualitative evidence changes what researchers can understand, explain, test, or conclude.
Read Guide →Systematic reviews can provide a stronger basis for conclusions than individual studies, but their label alone does not make them more trustworthy. What matters is how the review was conducted and the quality, relevance, and consistency of the evidence it synthesizes.
Read Guide →Adding studies or participants to a meta-analysis can improve statistical precision, but size alone does not determine evidential strength. The quality, relevance, consistency, and completeness of the underlying evidence still matter.
Read Guide →Some corrections fix minor errors. Others change the result you were relying on. Learn how to reassess the study and update your own claims when the corrected evidence is materially different.
Read Guide →Research studies do not always reach the same conclusion, and disagreement does not automatically mean that one study is wrong. Learn how to compare apparently conflicting findings and judge what the wider body of evidence actually supports.
Read Guide →A striking new study can change what researchers think, but publication date alone does not give it authority over everything that came before. The important question is how much the new evidence should change your confidence in the existing conclusion.
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