Services

Statistical Consulting for Research and Organizations.

For researchers and organizations who need a statistical partner, and for students who need expert support within their university’s rules. Every project starts with a free review, and a second statistician checks the work before it reaches you. Prefer to learn it yourself? Explore mentorship.

Researchers and students

Research and Academic Work

Consulting and statistical support for theses, surveys, and papers.

Thesis and Dissertation Support

  • Help choosing and justifying your methods: descriptive statistics, reliability, regression, mediation and moderation, and SEM, CFA, or PLS where the data supports it.
  • Checks on your analysis, assumptions, and tables before you submit.
  • Every step explained, with syntax and notes you can rerun, so you can defend the work in a viva or supervisor meeting.
  • Offered where your university allows outside statistical help. You remain the researcher and the author, and you declare our support if your rules require it.
  • Want to learn to run the whole study yourself? The Research Cohort teaches it from the first question.

Survey and Questionnaire Design

  • Question wording, scale choice, form logic, coding plans, and a sampling check.
  • Pilot review before you go to the field.
  • Already collected the data? We assess what it can credibly support.
  • Best before data collection, or when a weak form could undermine the analysis later.

Manuscript and Reviewer Support

  • Method justification, journal-ready tables, and clear results text.
  • Point-by-point answers to statistical reviewer comments.
  • We report what the data shows. We do not inflate claims or guarantee acceptance.
  • Best for authors preparing a submission or a revision.

Research Consultation

  • A focused session to test whether the question, data, design, and deadline can support a credible analysis.
  • You leave with a recommended method and a realistic next step.
  • Best when you need a second opinion or a plan before committing to a larger project.

Organizations

Data Work for Organizations

For NGOs, development projects, hospitals, businesses, and government teams.

Evaluation and M&E

  • Baseline, midline, and endline survey design and analysis.
  • Indicator tracking, results-framework measures, and program evaluation.
  • Reports written for donors, managers, and boards, with methods and limits stated.
  • Best for NGOs, development projects, and public programs.

Dashboards and Reporting

  • Power BI and Excel dashboards built from your data sources.
  • Recurring monthly or quarterly reports that use the same definitions every time.
  • Handover notes so your team can refresh and maintain the dashboard.
  • Best for organizations that report the same numbers again and again.

Specialist data

Specialist Analysis

For projects that need machine learning, omics data, or careful handling of clinical data.

Machine Learning and Predictive Modeling

  • Classification, regression, and risk prediction models in Python or R.
  • Proper train and test splits, cross-validation, and honest performance measures.
  • A plain explanation of which variables matter and where the model can fail.
  • Best for research questions or organizational data where prediction is the goal.

Bioinformatics and Health Data

  • RNA-seq, single-cell, and other omics workflows, from processing to interpretation.
  • Clinical, hospital, and public health datasets, including regression and survival analysis.
  • Reproducible code and careful handling of sensitive records.
  • Best for health researchers, clinicians, and labs.

How it works

How a Project Works.

01

Tell Us About the Project

Send the topic, where your data stands, the deadline, and any software you need. We check whether it fits and whether the method is clear enough to start.

02

Agree the Plan

We confirm the question, the method, what you will receive, what a revision covers, and the timeline before any work begins.

03

We Run It and Explain It

You get tables, outputs, and short notes on what was done, why, and what the results mean.

04

Revise If Needed

One scoped revision covers comments from your supervisor, reviewer, or client, without restarting the project.

Methods

What We Support, and What We Decline.

Supported

  • Descriptive statistics and reliability (Cronbach’s alpha, item-total correlations)
  • Regression, mediation, and moderation
  • SEM, CFA, and PLS-SEM where the data and sample size support it
  • Survey design review and post-collection data assessment
  • Program evaluation and M&E indicator analysis
  • Power BI and Excel dashboards
  • Machine learning: classification, prediction, and model validation
  • Omics and clinical data analysis
  • R, Python, SPSS, Stata, and Excel workflows

Declined

  • Fabricated or manipulated results
  • Methods chosen only to reach significance (p-hacking)
  • Work that misrepresents your role, or ours
  • Guaranteed publication, grades, approvals, or funding

Deliverables

What a Project Can Include.

  • An analysis plan, confirmed before the main work starts
  • Tables, charts, and cleaned data files (.sav, .rds, .csv, or similar)
  • Code and syntax files (.sps, .R, .do, or Python)
  • Word-ready tables and short notes on what the results mean
  • Reports for donors or managers, and dashboard files (.pbix or Excel)
  • A methods memo explaining model choice and limits
  • One scoped revision for comments from your supervisor, reviewer, or client

A replication package (code, cleaned outputs, and a methods memo) is available on request, so you can reopen and defend the work later.

Out of scope

What We Turn Down.

No Ghostwriting

We never write theses, papers, or assignments for anyone. Students learn to do the work themselves; we teach, check, and explain.

No Fabricated Results

We never invent data, manufacture findings, or cut corners to reach a result.

No Outcome Guarantees

We do not promise publication, grades, approvals, or funding. Those depend on other people’s judgment.

Stat.BD