Introduction to Data and Statistics (Video)

Vocabulary flashcards derived from the lecture notes on data, statistics, data structures, and measurement concepts.

Statistics (as a lens)

Statistics is a lens or perspective for approaching the world to solve problems and make decisions under uncertainty; the science of variability.

Variability

The property that observations differ; variability is universal, and patterns emerge when we zoom out.

Statistical Investigative Cycle

A five-step framework for data analysis: Problem, Plan, Data, Analysis, Conclusion.

Problem

What you want to learn or answer with statistical analysis.

Plan

The methods and study designs chosen to address the problem.

Data

How you measure and store information about observations.

Analysis

Which analyses to conduct and how to perform them.

Conclusion

How the results answer the problem and inform new questions.

Tidy Data

Observations as rows and attributes as columns;

Observation

A single unit of data; a row in a dataset.

Attribute

A feature or variable; a column in a dataset.

Header Row

The first row containing variable names; not an observation.

Quantitative Data

Numeric data representing quantities; may include decimals.

Categorical Data

Data with values drawn from categories or labels.

Rating Scale Data

Ordered categories (e.g., strongly disagree to strongly agree); ordinal data.

Ordinal Data

Data with a natural order; treated similarly to rating scale data.

Text Data

Open-ended or textual responses written as words or sentences.

Time Series Data

Data where the value corresponds to a moment in time (dates, times, etc.).

Reliability

The degree to which a measurement reflects the true world characteristic; consistency of data.

Measurement Process

Methods and procedures for collecting data (devices and protocols).

By-hand Measure

A manual or informal measure; often less reliable than standardized methods.

Blood Pressure

A quantitative health measure in mmHg used to illustrate typical vs. atypical values.

Typical Value

The central or common value in a distribution (e.g., BP around 140).

Model

A simplified representation of reality used to predict or compare data.

Statistical Testing

Assessing whether data align with model predictions and identifying inconsistencies.

Uncertainty

The lack of certainty; incorporate uncertainty into estimates and decisions.

What Was Compared?

A saying to remind you to consider the basis of the comparison behind claims.

Who’s Not Here?

A saying highlighting the representativeness of a sample and data origin.

Incorporate ‘ish’-ness

A saying urging inclusion of uncertainty and potential biases in estimates.