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Analysis can change after data collection, but the evidence itself has boundaries. Once collection ends, researchers may be unable to recover missing variables, baseline states, observations, exposures, or procedures that never occurred.
Read Guide →A brief feasibility conversation can prevent weeks of designing a study around participants, data, expertise, or resources that are not actually available. Informal confirmation is useful when external dependencies could materially constrain the design.
Read Guide →A research plan can make perfect methodological sense and still be impossible to execute. A realistic plan survives contact with actual participants, resources, permissions, workloads, timelines, and institutional constraints.
Read Guide →A promising research idea is not automatically ready for study design. Before proceeding, test whether the problem, question, contribution, evidence needs, and practical conditions are sufficiently defensible.
Read Guide →A clear and important research question can still fail if the evidence required to answer it is inaccessible, unobservable, too weak, or unrealistic to obtain. Test the evidence pathway before committing to the study.
Read Guide →Research can generate new information without generating useful knowledge. Before conducting a study, ask whether knowing the answer would meaningfully change understanding, explanation, decisions, methods, or future research.
Read Guide →You do not need to personally possess every skill a study requires. You do need a credible plan for ensuring that the necessary substantive, methodological, technical, and operational expertise is available when the research needs it.
Read Guide →A study can be scientifically strong yet impossible to execute with the resources available. Before committing to the design, identify every resource the evidence pathway requires and test whether the real budget can support it.
Read Guide →Participants give more than data. They may give time, effort, privacy, convenience, comfort, and sometimes accept risk. A defensible study should minimize those burdens and justify what remains by the value of the knowledge sought.
Read Guide →A worthwhile study should not depend entirely on confirming the result you hope to find. Ask what would be learned if the main hypothesis is unsupported and whether the design can distinguish informative evidence from simple uncertainty.
Read Guide →Every research design depends on assumptions about participants, data, measurement, implementation, analysis, and logistics. The dangerous assumptions are those that are both uncertain and capable of undermining the study if they prove wrong.
Read Guide →Uncertainty does not need to reach zero before further research becomes unnecessary. Before collecting new data, ask whether existing evidence already supports the relevant conclusion or decision well enough.
Read Guide →Participants are not raw material for a research project. Before asking for their time, information, effort, or privacy, make sure you can explain why their contribution is necessary and what worthwhile knowledge it could help produce.
Read Guide →Detailed study design should begin only after the core logic of the research idea is sufficiently stable. A serious unresolved problem with the question, evidence, contribution, feasibility, or ethics can make methodological planning premature.
Read Guide →You usually need some engagement with the literature before finalizing a research question, but you do not necessarily need a complete literature review first. In many studies, searching, reading, and question development proceed iteratively.
Read Guide →A literature search can legitimately change your research question when it reveals better terminology, existing evidence, conceptual problems, feasibility constraints, or a more meaningful problem. The key is to distinguish evidence-informed refinement from arbitrary or outcome-driven changes.
Read Guide →A search strategy is more than a list of keywords. It is a deliberate plan connecting your research question to searchable concepts, appropriate terminology, information sources, query structure, and decisions about how the search will be tested and documented.
Read Guide →Not every literature search needs to be systematic. The appropriate level of rigor depends on what the search is supposed to accomplish, how the literature will be used, and how strongly your conclusions depend on identifying the relevant evidence.
Read Guide →Systematic, reproducible, and comprehensive describe different qualities of literature searching. A search can be reproducible without being comprehensive, systematic without achieving perfect coverage, or extensive without being sufficiently documented to reproduce.
Read Guide →More searching is not automatically better searching. A literature search can become disproportionately comprehensive when additional sources and search routes add substantial workload without meaningfully improving the evidence needed for the question.
Read Guide →A collection of individually relevant papers can still misrepresent a field if the search systematically favors some evidence over other evidence. Relevance tells you whether a paper belongs; it does not tell you whether the literature you found represents the evidence you needed to see.
Read Guide →Knowing many papers gives you familiarity with individual studies. Understanding a literature means you can explain how those studies relate, where evidence converges or conflicts, what methods shape the findings, and what remains uncertain.
Read Guide →A thesis or dissertation needs a defensibly thorough literature search, not an impossible search for every publication. Learn what adequate coverage looks like and when you can reasonably stop searching.
Read Guide →A journal article needs enough literature searching to support its claims and position its contribution accurately, but not every article requires an exhaustive search. The appropriate depth depends on what the paper is trying to establish.
Read Guide →Before designing a new study, search far enough to understand what is already known and how previous researchers investigated the problem. The goal is not exhaustive retrieval but preventing avoidable design errors and unnecessary research.
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