STAT-103: APPLIED STATISTICAL MANIPULATION FOR BEGINNERS

F.A.R.T. E.D. (Education Department)
Moto: Cogito ergo PEPĒDĪ.

STAT-103: APPLIED STATISTICAL MANIPULATION FOR BEGINNERS

Autumn Term – 2025, 2026, 2027 & 2028
School for Highly Improbable Theories (acronym pending)

Course Instructor

Una Likely, Chief Probability Analyst and Statistical Denier

Office: Room 15A, Probability Wing (accessible only when the corridor isn’t experiencing temporal displacement)
Office Hours: Mondays 14:00-16:00 and by statistical appointment (probability of availability: 67.3%)
Email: una@ifart.co.uk

Course Overview

This foundational course introduces students to the art and science of persuasive numerical presentation. Students will learn to transform inconvenient data into compelling narratives through strategic data interpretation, confidence interval gymnastics, and the subtle manipulation of statistical significance. The course emphasises practical applications in academic research, institutional reporting, and existential crisis management. By the term’s end, students will be equipped to make any dataset appear to confess to virtually anything while maintaining the appearance of mathematical rigour.

This course operates under the fundamental principle that statistics, like quantum mechanics, behave differently when observed by someone with a predetermined conclusion.

Learning Outcomes

By the end of this course, students should be able to:

Calculate confidence intervals with sufficient flexibility to encompass any desired outcome
Apply advanced rounding techniques to achieve statistical significance from marginal p-values
Construct correlation matrices that imply causation without explicitly stating it
Identify and strategically relocate outliers to alternative dimensions
Generate compelling visualisations that tell the story the data ought to have told
Estimate the probability of improbable events with sufficient precision to confuse peer reviewers

Prerequisites

MTHS-101: Basic Mathematics (or demonstrated ability to count beyond seventeen)
A flexible relationship with objective reality
Signed waiver acknowledging that exposure to manipulated statistics may cause chronic scepticism
Note: Students with pre-existing conditions of mathematical integrity should consult the instructor before enrolling

Required Materials

“Statistical Methodology for the Morally Flexible” (Likely, U., 2024)
“P-Hacking: A Practical Guide to Academic Success” (Bendwell, N., 2023)
Scientific calculator (preferably one that occasionally provides optimistic results)
Graph paper (minimum 0.7mm squares for precision outlier placement)
Red pen (for marking “significant” results regardless of actual significance)
Emergency probability charts (laminated, for spills during tea-related statistical crises)

Assessment

20% Weekly Problem Sets (creative solutions encouraged, mathematical accuracy optional)
25% Midterm Examination (open book, open mind, closed conscience)
15% Data Manipulation Laboratory Practical (bring your own dataset to torture)
25% Final Project: “Making Numbers Dance” – Students must convince a randomly assigned dataset to support three contradictory conclusions
15% Class Participation (measured by frequency of sceptical facial expressions and audible sighs)

Weekly Schedule

Week 1: Introduction to Statistical Elasticity
Basic principles of data interpretation, the observer effect in academic research, and the probability that this course will make sense by the end.

Week 2: Confidence Intervals and Creative Rounding
Techniques for expanding margins of error to encompass preferred outcomes; the art of strategic decimal placement; when 2.97 becomes “approximately 3.0.”

Week 3: Correlation Theatre
Creating compelling relationships between unrelated variables, advanced scatter plot choreography, and making causation hide behind correlation’s skirts.

Week 4: The Care and Feeding of Outliers
Identifying problematic data points, outlier relocation services, and statistical witness protection programmes.

Week 5: P-Value Archaeology
Excavating significance from the ruins of failed experiments; mining methodologies for hidden statistical gems; the 0.049 miracle.

Week 6: Sample Size Flexibility
Determining optimal sample sizes for predetermined conclusions; the principle of statistical convergence through persistence; when n=3 becomes “extensive study.”

Week 7: Midterm Examination Week
Assessment of student progress in abandoning mathematical principles whilst maintaining academic dignity.

Week 8: Advanced Visualisation Techniques
Chart manipulation for maximum emotional impact; colour psychology in data presentation; making pie charts more persuasive than actual pies.

Week 9: Regression to the Convenient
Linear regression for non-linear minds; forcing curves through predetermined points; making data sets confess under statistical interrogation.

Week 10: Error Bars and Uncertainty Principles
Managing measurement uncertainties to achieve desired certainties; error propagation as a creative writing exercise; confidence in the face of statistical evidence.

Week 11: Meta-Analysis and Cherry Orchard Management
Combining studies to achieve pre-selected outcomes; statistical fruit selection methodologies; harvesting significance from fields of failure.

Week 12: Professional Statistical Ethics (or: How to Sleep at Night)
Maintaining plausible deniability in academic publications, peer review evasion techniques, and statistical confession protocols.

Institutional Policies

Academic Integrity Statement: Students are expected to maintain the highest standards of statistical rigour while avoiding outright numerical inaccuracies. Plagiarism of statistical manipulations is discouraged, though students may freely borrow methodological inspiration from previous practitioners of creative mathematics.

Laboratory Safety Protocols:

Never attempt to divide by zero without proper supervision
Maintain a safe distance from confidence intervals exceeding 127%
Report all instances of spontaneous statistical significance to the Department of Improbable Mathematics immediately
Emergency eyewash stations are available for exposure to particularly shocking correlation coefficients

Assessment Appeals Process: Students disputing marks may submit a statistical analysis demonstrating why their grade should be different. Appeals supported by properly manipulated data will receive serious consideration.

Attendance Policy: Class attendance is recorded using a modified Schrödinger approach – students are simultaneously present and absent until directly observed by the instructor. This quantum attendance state collapses upon eye contact or audible snoring.

Warning Labels

Completion of this course may result in chronic inability to accept published statistics at face value (probability: 94.7% ± 23%)
Students have reported developing involuntary eye-twitching when encountering properly conducted statistical analyses
Side effects may include: excessive graph scrutinising, compulsive p-value checking, and a tendency to recalculate restaurant bills multiple times
The Department of Statistical Improbabilities accepts no responsibility for existential crises arising from the realisation that most published research is statistically questionable

Contact Information

For statistical emergencies occurring outside office hours, students may contact the Department of Improbable Mathematics crisis hotline (Extension 404 — Error Not Found). Please note that emergency statistical consultations require a minimum of three chocolate hobnobs and a properly brewed cup of Earl Grey as a consultation fee.

Course Reference: STAT-103-AU25-UL
Last Updated: 15/08/2025 (subject to revision based on statistical convenience)
Approval: Certified by the Academic Standards Committee with a 73% confidence interval that this course meets minimum educational requirements

STAT-103: APPLIED STATISTICAL MANIPULATION FOR BEGINNERS