Statistical Analysis & Hypothesis Testing
Choose methods that fit the study design and variable types, then report assumptions, effect sizes and uncertainty.
Explore this serviceAsk better questions of your data.
Work from clean data and explicit assumptions to appropriate statistical methods, clear visualisations and careful interpretation.
Areas of assistance
Begin with the research question, variable definitions and study design. We help review data quality, method choice, statistical assumptions and interpretation. Guidance distinguishes a calculated result from a supported conclusion and includes limitations, uncertainty and reproducibility. Participant data should be anonymised before it is shared.
Topics we can explore
- Descriptive and inferential statistics
- Hypothesis testing
- Regression and correlation
- ANOVA
- Chi-square tests
- T-tests
- Survey data analysis
- Data cleaning
- Missing data
- Coding questionnaires
- Research data interpretation
- Reproducible analysis
- Statistical visualisation
- Tableau
- Power BI
- Excel statistics
- Exploratory analysis
- Accessible charts
Typical learning or professional deliverables
- Explanations and practical walkthroughs matched to your current work
- Structured feedback and a review checklist
- Agreed documentation or reference examples
Deliverables depend on the agreed scope. Academic work remains your own; engineering and financial guidance does not replace qualified professional approval.
Choose your area of focus.
Survey Data & Research Interpretation
Prepare survey data consistently and distinguish what a sample supports from claims it cannot establish.
Explore this serviceData Visualisation & Analytics
Create readable charts and dashboards by matching the visual form to the analytical question and the audience.
Explore this serviceQuestions about Statistics & Data Analytics
How do we choose an appropriate statistical test?
We review variable types, sample design, dependence, sample size and assumptions, then explain suitable options and their limits.
Can a significant result prove causation?
No. Causal claims depend on design and assumptions. A p-value alone does not establish causal effects or practical importance.
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