Each year of our project, our friends at the NINDS add a new cohort of coLABs to help us make even more units to teach the principles of scientific rigor. Each coLAB brings their own expertise to the table, and their work helps us bridge into new realms of the skills every modern researcher needs.
At this stage in the project, we’re ready to share the units we’re currently developing, and we want to hear from you. See something missing? Send us a note about it. Want to really get your pet project through the door? Apply for the next round of METER grants, due October 10, 2024!
- Overview of the Research Process
- How to get unstuck at every stage of experimental research and get further acquainted with the ins and outs of rigorous practice.
- Improve your research rigor with randomization
- Controlling for bias, confounders, through proper randomization methods.
- Setting exclusion criteria in lab experiments
- Establishing specific criteria for experimental data to build legitimate standards for inclusion/exclusion.
- Causation versus Correlation
- Taking a rigorous approach to gleaning causal understanding from studies and different lines of evidence; recognizing how causation can be inferred without controlled RCTs.
- How to Design A Good Laboratory Experiment: Importance of Controls
- Effective usage of experimental controls when designing, debugging, and deciphering studies.
- Writing Readable & Correct Scientific Code
- Applying techniques to make your code modular, maintainable, and verifiably correct.
- Detecting Errors in Your Analyses
- Pinpoint problems in code and methods by visualizing inputs/outputs, working through examples, and testing simulations in order to ensure that results are valid.
- Stop fooling yourself! Diagnosing and Treating Confirmation Bias
- Train your intuition for the ways in which our expectations influence how we conduct research and interpret findings, and learn the habits of effective expert experimenters.
- Discerning Biological Outcomes from Experimental Artifacts
- Understand the functioning of an experimental system to correctly decipher signals and avoid misinterpreting the effects of your interventions.
- Formulating a Valid Research Question
- Use composition and evaluation frameworks to create a well-specified and rigorous research question.
- Executing Structured Searches of the Literature
- Search the literature with confidence and reliability using database/keyword selection, index terms, filters, and Boolean operators..
- Assessing the credibility of a body of literature
- Evaluate published work for imprecision, inconsistencies, indirectness, with implications for correct interpretation of the collective findings.
- Sample Size – How much data is enough for your experiment?
- Calculating sample size to achieve power.
- Evaluate your evaluation methods! A key to meaningful inference
- Check the assumptions of data analyses, adjusting them to align with the desired questions, and drawing the appropriate conclusions.
- Reproducible exploratory analysis: Mitigating multiplicity when mining data
- Employing good practices in data mining to produce correct interpretations of the evidence.
- Putting the p-value in context: p<0.05, but what does it REALLY mean?
- Knowing what p-values mean and what they don’t mean, for comprehending and communicating scientific results precisely.
- Identifying Rigor Red Flags in Research Reports
- What makes a published study reliable? Learn how to read between the lines to figure out what that study was actually about and what it means.
- Effective Data Visualization for Research Communication
- Communicate your scientific findings clearly and beautifully! Avoid having people’s eyes glaze over when they walk by your poster.
- Transparent Data Sharing to Advance Discovery
- Annotate, organize (and share) your data for yourself, your collaborators, and future researchers.
- Defining and distinguishing between hypothesis generating and hypothesis testing research
- Identify where a study falls on the exploratory-confirmatory axis and use rigorous methods appropriate to each.
- Adapting to change: Best practices for method standardization and laboratory results recording
- Follow written protocols and keep accurate lab journals to efficiently execute a research project, and maintain a verifiable record of the findings.
- Transparency and Accuracy in Reporting Study Findings
- Report your findings using community standards to maximize your impact and minimize misleading readers.
- The perils of outcome switching
- Be aware of the tricky ways that a research project’s goals might slip in scope and learn to manage the outcomes.
We love talking about this stuff, so we’ll ask again. See something missing? Send us a note about it. Want to really get your pet project through the door? Apply for the next round of METER grants, due October 11, 2024!
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