Statistics Final Year Topic: Power to Detect Interaction in a Two-Factor Experiment

This Statistics final year project uses simulated factorial outcomes with declared main and interaction effects to investigate a specific question in design of experiments. The analysis is designed around known generating conditions so that the behaviour of competing statistical procedures can be checked.

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Why choose this project topic?

This study makes factorial interaction an explicit, reproducible comparison. Working with simulated factorial outcomes with declared main and interaction effects lets you separate the target quantity from its estimate and explain when a statistical conclusion depends on assumptions.

How does allocation across a two-factor design affect power for a prespecified interaction effect?

Agree the scenario ranges, sample sizes and reporting measures for factorial interaction before running the study.

Proposed project objectives

01Specify the data-generating conditions for simulated factorial outcomes with declared main and interaction effects.
02Implement a reproducible analysis of factorial interaction with documented software settings.
03Evaluate the estimates and uncertainty relevant to this question: How does allocation across a two-factor design affect power for a prespecified interaction effect?

A suggested research approach

Compare balanced and feasible unbalanced allocations at a fixed total size. Fit the intended model, distinguish interaction from main-effect tests and evaluate interval coverage as well as rejection rates. Write the analysis before inspecting favourable runs, record random seeds where simulation is used, and keep generated study data distinct from observed field data.

What you will need

A written design for simulated factorial outcomes with declared main and interaction effects
Statistical software supporting design of experiments and reproducible scripts
A supervisor-agreed range of scenarios and computational budget

Keep your project scope clear

An interaction depends on the response scale and model; absence of significance is not evidence that all effects are additive.

Statistics project chapter outline

Use this outline as a starting point. You can edit the chapter titles to match your department’s format during setup.

Chapter 1Introduction
Chapter 2Literature Review
Chapter 3Theory and Methodology
Chapter 4Results and Applications
Chapter 5Summary, Conclusion and Recommendations

Turn this topic into your own final year project.

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