hypothesis-testing_skill

Overview

This skill guides researchers in turning observations into testable, falsifiable hypotheses and concrete experimental plans. It emphasizes logical rigor, operational precision, and clear falsification criteria to support reproducible empirical tests. Use it to produce null and alternative hypotheses, map variables, and choose an appropriate design for causal inference.

How this skill works

The skill deconstructs an observation, refines a focused research question, and constructs H0 and H1 with an explicit causal mechanism. It then specifies roles and measurements for IVs, DVs, controls, and potential confounds, selects an experimental or observational design, and defines exact falsification criteria. Finally, it highlights mitigation strategies and checkpoints for sensitivity, feasibility, and power analysis.

When to use it

• Formulating testable hypotheses from qualitative or quantitative observations
• Designing controls and operational definitions before data collection
• Specifying falsification rules and pre-analysis criteria for empirical studies
• Choosing between RCT, quasi-experiment, or observational design for causal claims
• Preparing a pre-analysis plan or power analysis for an experiment proposal

Best practices

• Write H0 and H1 so the alternative is falsifiable by specific observable outcomes
• Define each variable with a concrete measurement method and scale
• State the assumed mechanism linking IV to DV and list boundary conditions
• Identify likely confounds early and specify control or randomization strategies
• Include a priori falsification criteria and decision rules for rejecting H1

Example use cases

• Turning a lab observation (drug reduces symptom intensity) into H0/H1, mechanism, and RCT plan
• Designing a quasi-experiment when random assignment is infeasible (policy evaluation)
• Operationalizing behavioral measures (e.g., attention as reaction time and error rate)
• Defining mediator and moderator tests for a proposed causal pathway
• Drafting explicit falsification patterns for observational correlations