Short Course

Learning Goals

• Understand the purpose of scientific research
• Distinguish evidence from belief and anecdote
• Recognise uncertainty as an essential feature of science

Core Concepts

• Science as error reduction, not truth-finding
• Replication and cumulative knowledge
• Limitations of single studies

Learning Goals

• Develop clear, testable theoretical claims
• Avoid vague or unfalsifiable concepts

Core Concepts

• What constitutes a theory
• Constructs vs operationalisations
• Causal vs descriptive explanations

Practical Steps

1. Identify a phenomenon of interest
2. Define constructs precisely
3. Map expected relationships between variables
4. Identify observations that would challenge the theory

Learning Goals

• Translate theory into answerable research questions
• Distinguish exploratory from confirmatory research

Core Concepts

• Descriptive vs inferential questions
• Scope and feasibility
• Uncertainty reduction

Practical Steps

• Write the question in plain language
• Specify population, context, and outcomes
• Decide what uncertainty the study aims to reduce

Learning Goals

• Formulate informative and falsifiable hypotheses
• Avoid post hoc predictions

Core Concepts

• Directional vs non-directional hypotheses
• Null hypotheses as statistical tools
• Principles of machine-readable hypothesis

Practical Steps

• Link each hypothesis explicitly to theory
• Specify direction and expected magnitude where possible
• Identify plausible alternative explanations

Learning Goals

• Select designs appropriate to the research question
• Understand the statistical premise of validity and reliability

Core Concepts

• Experimental, quasi-experimental, and observational designs
• Reliability and validity
• Measurement error in sports science

Practical Steps

• Justify design choices
• Pilot test procedures
• Document protocols clearly

Learning Goals

• Understand the purpose of preregistration and registered reports
• Use preregistration to improve study planning and transparency

Core Concepts

• Researcher degrees of freedom
• HARKing
• Methods of registering your research

Practical Steps

• Preregister hypotheses and primary outcomes
• Specify inclusion/exclusion criteria
• Document planned analyses

Learning Goals

• Understand why small samples increase uncertainty
• Plan studies that are informative and control for error

Core Concepts

• Type I and Type II error
• Statistical power
• A priori vs post-hoc power

Practical Steps

• Justify sample size explicitly
• Define the smallest effect size of interest
• Transparently acknowledge feasibility constraints
• understand limitations in small samples

Learning Goals

• Interpret statistical results correctly
• Avoid dichotomous thinking

Core Concepts

• p-values as continuous evidence
• Confidence intervals
• Multiple comparisons and error inflation

Practical Steps

• Conduct preregistered analyses first
• Clearly label exploratory analyses
• Focus on estimation over significance

Learning Goals

• Enable others to evaluate and reproduce the study
• Reduce ambiguity in methods and analyses

Core Concepts

• Open data and materials
• Reproducible workflows
• Computational transparency

Practical Steps

• Share data, code, and materials where possible
• Use open repositories (e.g., OSF)
• Document data processing steps

Learning Goals

• Report results accurately and completely
• Avoid selective reporting

Core Concepts

• Reporting guidelines (CONSORT, STROBE)
• Effect sizes and uncertainty
• Null and negative results

Practical Steps

• Report all preregistered outcomes
• Include limitations prominently
• Avoid spin in abstracts and conclusions

Learning Goals

• Communicate findings responsibly
• Avoid overstating implications
• understand replication findings of regressed effect sizes

Core Concepts

• Statistical vs practical significance
• External validity
• Single-study limitations and inflated estimates

Practical Steps

• Use probabilistic language
• Avoid causal claims without causal designs
• Frame findings as contributions, not conclusions

Learning Goals

• Understand how scientific knowledge accumulates
• Appreciate the role of replication

Core Concepts

• Direct vs conceptual replication
• Meta-analysis
• Research waste

Practical Steps

• Encourage replication attempts
• Share data for reuse
• Engage with replication initiatives

Learning Goals

• Reflect on incentives and research culture
• Align practices with scientific values

Core Concepts

• Publication bias
• Career incentives
• Responsible authorship

Practical Steps

• Prioritise quality over novelty
• Mentor responsibly
• Model good scientific practices