Statistics Final Year Topic: Bias and Precision of Trimmed Means under Controlled Data Contamination

This Statistics final year project uses mixture-distribution simulations with a declared clean component to investigate a specific question in robust statistics. 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 trimmed mean robustness an explicit, reproducible comparison. Working with mixture-distribution simulations with a declared clean component lets you separate the target quantity from its estimate and explain when a statistical conclusion depends on assumptions.

How do trimming proportions affect estimation of a clean population centre when a sample contains controlled contamination?

Agree the scenario ranges, sample sizes and reporting measures for trimmed mean robustness before running the study.

Proposed project objectives

01Specify the data-generating conditions for mixture-distribution simulations with a declared clean component.
02Implement a reproducible analysis of trimmed mean robustness with documented software settings.
03Evaluate the estimates and uncertainty relevant to this question: How do trimming proportions affect estimation of a clean population centre when a sample contains controlled contamination?

A suggested research approach

Vary contamination fraction and severity while retaining a clear target centre. Compare mean, median and trimmed estimators by bias and mean squared error across independent simulation runs. 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 mixture-distribution simulations with a declared clean component
Statistical software supporting robust statistics and reproducible scripts
A supervisor-agreed range of scenarios and computational budget

Keep your project scope clear

The target centre must be defined before comparison; robust estimation is not equivalent to deleting inconvenient observations.

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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