Statistics Final Year Topic: False-Positive Rates of Z-Score and Median-Based Outlier Rules in Skewed Data

This Statistics final year project uses samples from fully specified uncontaminated and contaminated distributions to investigate a specific question in exploratory 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 outlier detection an explicit, reproducible comparison. Working with samples from fully specified uncontaminated and contaminated distributions lets you separate the target quantity from its estimate and explain when a statistical conclusion depends on assumptions.

How do skewness and sample size affect the rate at which simple outlier rules flag uncontaminated observations?

Agree the scenario ranges, sample sizes and reporting measures for outlier detection before running the study.

Proposed project objectives

01Specify the data-generating conditions for samples from fully specified uncontaminated and contaminated distributions.
02Implement a reproducible analysis of outlier detection with documented software settings.
03Evaluate the estimates and uncertainty relevant to this question: How do skewness and sample size affect the rate at which simple outlier rules flag uncontaminated observations?

A suggested research approach

Define thresholds before simulation and compare z-score and median-based rules. Separate false flags in clean data from detection in contaminated data and record behaviour when robust scale estimates vanish. 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 samples from fully specified uncontaminated and contaminated distributions
Statistical software supporting exploratory statistics and reproducible scripts
A supervisor-agreed range of scenarios and computational budget

Keep your project scope clear

A statistical outlier flag is not proof of measurement error, fraud or a reason to exclude a record.

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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Generate the Complete Project Generation uses your word balance. Review the draft and supply your own verified research findings.