Resource centre
Practical material on study design, analysis and reporting. The sample size calculator and FAQs are available now; guides, tutorials and templates are being written.
Resource categories
- Planned
Statistical guides
Plain-language guides to methods and their assumptions.
- Planned
Research methodology articles
Study design, sampling and measurement.
- Planned
R and Python tutorials
Worked code examples with open data.
- Planned
Survey design resources
Question wording, scales and sampling checklists.
- Planned
Data analysis examples
End-to-end examples using public datasets.
- Available
Statistical calculators
Sample size for a proportion is available now. More to follow.
- Planned
Downloadable templates
Analysis plans, questionnaire and report templates.
- Available
Frequently asked questions
Answers about working with ezyStat.
Sample size calculator
How many completed responses do you need to estimate a percentage with a given precision? Adjust the inputs and the result updates as you type.
Completed responses needed
1,068
- Base sample, n₀ = z²p(1−p)/e²
- 1067.1
- After design effect × 1
- 1067.1
This applies to estimating one proportion. Comparing subgroups, measuring change or estimating means needs a different calculation. Treat the result as a starting point for discussion, not a final design.
Articles in preparation
These guides are planned. Titles and summaries show the intended scope; they will be published here as they are completed.
- Survey designPlanned
How to determine sample size for a survey
Margin of error, confidence level, subgroups and the effect of weighting, worked through with examples.
- Statistical guidesPlanned
Choosing the right statistical test
A practical route from your research question and data type to an appropriate test or model.
- Research methodologyPlanned
Market research versus feasibility studies
What each one answers, when you need both, and how they fit together.
- R and Python tutorialsPlanned
Mixed-effects models in R: a first walkthrough
Fitting and interpreting a random-intercept model with lme4, using an open dataset.
- Survey designPlanned
Survey weighting without the mystery
Raking, design effects and effective sample size, explained with a small example.
- R and Python tutorialsPlanned
Cleaning messy data in Python
A reproducible pandas workflow for duplicates, missing values and inconsistent categories.
Frequently asked questions
About working with ezyStat.
How do projects usually start?
Send a short description of your project through the enquiry form. We reply to arrange an initial conversation, then send a written proposal setting out the scope, method, timeline and cost.
How are projects priced?
Most work is priced per project after scoping, so you know the cost before work begins. Ongoing support and training can be arranged at an agreed daily or hourly rate.
Should I send my data with my enquiry?
No. Please do not send datasets or personal information through the contact form. If a project goes ahead, we agree a secure way to transfer data and the terms under which it is handled.
Do you work with clients outside the UK?
Yes. We work with international clients remotely. Fieldwork outside the UK is arranged with qualified local partners, subject to project feasibility.
Will you write my thesis or assignment?
No. We provide consultancy, review and training so that you understand and can defend your own analysis, in line with your institution's academic integrity rules.
Which software will you use?
Usually R or Python. Where your organisation works in SPSS, Excel or Power BI, we can deliver in those tools so your team can maintain the work.
Have a research question or data challenge?
Tell us about your project and discover how ezyStat can help you design, analyse and interpret your data.