7 Checkpoints That Will Strengthen the Reproducibility of Your Research—Starting Today

A Crisis That Arrives at the Bench, Not Just in the Headlines

When Nature surveyed more than 1,500 scientists in 2016 and found that over 70 percent had tried and failed to reproduce another researcher's results, the reproducibility crisis moved from a theoretical concern to a documented reality. Since then, independent replication failures have been reported across psychology, cancer biology, economics, neuroscience, and nutrition science—fields that collectively shape public policy, clinical practice, and billions of dollars in research funding.

Yet for most working researchers—graduate students running their first independent experiments, postdoctoral fellows managing complex datasets, or junior faculty establishing new labs—the reproducibility problem can feel distant and structural, something for funding agencies and journal editors to sort out.

It is not. Reproducibility begins and ends at the level of individual research practice. The decisions made at the bench, the keyboard, or the field site—how an experiment is designed, how data are recorded, how statistics are selected and reported—determine whether another researcher can meaningfully replicate and build on your work.

The following seven checkpoints are drawn from methodological frameworks developed by organizations including the Center for Open Science, the NIH's Rigor and Reproducibility initiative, and documented case studies of research groups that have measurably improved their reproducibility rates. Each checkpoint is actionable, regardless of discipline or career stage.

Checkpoint 1: Pre-Register Your Study Before Data Collection Begins

Pre-registration is the practice of publicly documenting your research question, hypotheses, study design, and planned statistical analyses before collecting a single data point. Platforms such as OSF (Open Science Framework), AsPredicted, and ClinicalTrials.gov (for clinical research) provide free, time-stamped registration services.

The value of pre-registration is straightforward: it creates a clear distinction between confirmatory research (testing a pre-specified hypothesis) and exploratory research (generating new hypotheses from observed patterns). Without this distinction, a practice known as HARKing—Hypothesizing After Results are Known—becomes nearly invisible, both to reviewers and sometimes to the researchers themselves.

Labs that have adopted pre-registration as a standard practice consistently report two downstream benefits: their statistical analyses are more disciplined, and their published findings hold up better under independent scrutiny.

Action step: Before your next study, write a one-page pre-registration document specifying your primary hypothesis, your sample size and justification, your primary outcome measure, and your planned analysis approach. Post it to OSF before data collection begins.

Checkpoint 2: Calculate and Document Your Statistical Power

Underpowered studies—those with sample sizes too small to reliably detect the effect they are designed to measure—are one of the most pervasive contributors to irreproducible findings. A study with 40 percent statistical power will fail to detect a real effect 60 percent of the time. When underpowered studies do produce significant results, those results are disproportionately likely to be false positives.

Power analysis is not a formality. It is a foundational design decision. Tools such as G*Power (freely available and widely used in US academic settings) allow researchers to calculate the minimum sample size required to detect an effect of a specified size at an acceptable power level—conventionally set at 0.80 or higher.

Action step: Run a formal a priori power analysis for every study you design. Document your assumed effect size, alpha level, and power target. If your assumed effect size comes from a prior study, note whether that study was itself adequately powered—small-sample pilot studies often overestimate true effect sizes.

Checkpoint 3: Standardize and Document Every Protocol Step

Variability in experimental procedures is a primary driver of replication failure. When two researchers in the same lab—let alone two labs at different institutions—execute a protocol differently because the written instructions are ambiguous or incomplete, apparent inconsistencies in results are often artifacts of procedural variation rather than genuine scientific disagreement.

Standard Operating Procedures (SOPs) should be written at the level of detail that would allow a trained researcher with no prior exposure to your specific protocol to execute it correctly. This means specifying equipment model numbers, reagent concentrations, timing intervals, environmental conditions, and decision rules for handling unexpected observations.

Action step: Select one protocol currently used in your lab and rewrite it from scratch at maximum specificity. Then have a colleague who has not previously performed the procedure attempt to execute it using only your written instructions. The gaps they encounter are your reproducibility vulnerabilities.

Checkpoint 4: Implement Blinded Data Collection and Analysis Where Feasible

Observer bias—the tendency for researchers to record, interpret, or analyze data in ways that align with their hypotheses—is a well-documented threat to research validity that operates largely below the level of conscious awareness. Blinding, the practice of concealing condition assignments or expected outcomes from data collectors or analysts, is the most direct structural control for this bias.

Blinding is not exclusively a clinical trial concern. Behavioral researchers can blind coders to experimental conditions. Biologists can blind analysts to treatment groups during image quantification. Survey researchers can blind data cleaners to hypothesis-relevant variables.

Action step: For your current project, identify every stage of data collection and analysis where knowledge of the expected result could influence a judgment call. Implement blinding at each of those stages, even if only partially.

Checkpoint 5: Adopt a Rigorous Data Management Plan From Day One

Disorganized data is irreproducible data. When raw data files are overwritten, when variable names change between analysis versions, or when the link between a published figure and the underlying dataset cannot be reconstructed, reproducibility becomes impossible regardless of how well the original study was designed.

The NIH now requires Data Management and Sharing Plans for most funded research, but the underlying practices—version-controlled file storage, clearly named and documented datasets, separation of raw and processed data—are valuable independent of any funding requirement.

Action step: Establish a standardized folder structure and file naming convention for your lab before the next project begins. Use a version control system (Git repositories through platforms like GitHub or institutional equivalents) for analysis scripts. Store raw data in a read-only format that is never directly edited.

Checkpoint 6: Report Results With Full Transparency, Including Null Findings

Selective reporting—publishing only statistically significant results while filing away null or inconclusive findings—distorts the published literature and makes meta-analytic synthesis unreliable. This is not merely an ethical concern; it is a methodological one. A field whose published record systematically overrepresents positive findings will generate theoretical frameworks built on an unrepresentative evidence base.

Transparent reporting means presenting all pre-specified outcomes, not only those that reached significance. It means reporting exact p-values rather than threshold indicators. It means including effect sizes and confidence intervals alongside significance tests. And it means considering registered reports—a publication format in which peer review occurs before data collection, guaranteeing publication regardless of results.

Action step: Review your most recent manuscript or report. Verify that all pre-specified outcomes are reported, that effect sizes and confidence intervals are included for all primary analyses, and that any deviations from your original analysis plan are explicitly noted.

Checkpoint 7: Share Your Data and Materials

The ultimate reproducibility test is whether another researcher can independently verify your findings using your original data and materials. Open data practices—depositing datasets, analysis code, and study materials in publicly accessible repositories—make this verification possible.

Discipline-appropriate repositories in the US include OSF, Dryad, the Inter-university Consortium for Political and Social Research (ICPSR), and the NIH's National Center for Biotechnology Information (NCBI) for biological data. Many journals now require or strongly encourage data deposition as a condition of publication.

Action step: For your next publication, deposit your anonymized dataset and analysis code in an appropriate repository and include the repository link in your methods section. If data cannot be fully shared due to participant confidentiality or proprietary constraints, document exactly what is available and under what access conditions.

Reproducibility Is a Skill, Not a Compliance Exercise

The seven checkpoints above are not bureaucratic requirements imposed by journals or funding agencies—they are the practical expression of what it means to conduct research with methodological integrity. Labs that have systematically implemented these practices report not only higher reproducibility rates but also more efficient internal workflows, cleaner datasets, and stronger manuscript reviews.

At the Research Skill Center, we view reproducibility not as a crisis to be managed but as a competency to be developed. Like any research skill, it improves with deliberate practice, structured feedback, and a genuine commitment to the principles that make scientific knowledge trustworthy. The checkpoints in this article are a starting point. The standard they point toward—research that can be understood, verified, and built upon—is the foundation of every meaningful scientific advance.