BEA140 Week 1 Sources of data error and bias

Statistics

The science of learning from data, involving the collection, analysis, interpretation, and presentation of data to transform information into meaningful insights for decision-making

Statistical Process

Ask a question → Collect data → Prepare data → Analyse data → Interpret results → Communicate and decide

Descriptive Statistics

Methods used to organise and summarise the data we already have using graphs and numerical measures

Inferential Statistics

Methods that use sample data to draw conclusions, make estimates, test claims, and make predictions about a wider population under uncertainty

Population

The entire group of individuals or items we want to understand

Sample

A smaller group taken from the population that is actually observed

Population Parameter

A numerical value that describes a population (e.g. μ, σ)

Sample Statistic

A numerical value calculated from a sample (e.g. x̄, s) used to estimate a population parameter

Probability

A mathematical tool for studying randomness that moves from population to sample outcomes

Inference

The process of using sample data to draw conclusions about a population (moves from sample to population)

GIGO Principle

“Garbage In, Garbage Out” — poor-quality, biased, or inaccurate data lead to unreliable results

Categorical (Qualitative) Data

Data that describe qualities or categories and do not have numerical meaning

Numerical (Quantitative) Data

Data that represent values that can be counted or measured and allow arithmetic calculations

Nominal Data

Categories with no natural order or ranking (e.g. payment method)

Ordinal Data

Categories with a meaningful order but unequal or unknown distances between categories (e.g. satisfaction levels)

Discrete Data

Countable numerical values, typically whole numbers (e.g. number of customers)

Continuous Data

Measured numerical values that can take any value within a range (e.g. weight, income)