Statistical Concepts Overview

Representative Sample

Representative Sample

Ensures findings can be generalized to a larger population accurately

Statistical Thinking

Using data to make informed decisions and draw conclusions

Availability Heuristic

Assessing likelihood based on ease of recall

Deciding

Making informed choices in data analysis

Describing

Providing concise summaries and insights about data

Predicting

Anticipating future outcomes based on past data

Causality

Determining cause-and-effect relationships between variables

Aggregation

Summarizing and simplifying large datasets for analysis

Qualitative

Non-numeric information describing qualities of a subject

Quantitative

Numerical information analyzed mathematically

Binary

Data system with two possible values, typically 0 and 1

Ratio

Expresses relationship between quantities as a fraction

Integers

Whole numbers representing counts in a dataset

Discrete Measurements

Data with specific, distinct values, often whole numbers

Continuous Measurements

Variables with infinite values within a range

Reliability

Consistency of measurements

Validity

Ensuring measurements reflect the intended construct

Face Validity

Assessing if a measurement makes sense intuitively

Construct Validity

Checking if a measurement relates to others appropriately

Predictive Validity

Valid measurements should predict outcomes

Absolute Frequency

Number of times a value occurs in a dataset

Relative Frequency

Frequency divided by sum of all frequencies

Frequency Distribution

Shows frequency of possible values in a sample

Chart Junk

Unnecessary elements in graphs distracting from data

Colorblindness

A human limitation affecting data visualization

Data Visualization

Presenting data visually for easier comprehension

Cause and Effort

Sources of data: Rules of the World and Error

Mean

Describes central tendency of a dataset

Median

Summarizes data less sensitive to outliers

Mode

Describes central tendency of non-numeric datasets

Variability

Measures dispersion within a dataset

Z Scores

Number of standard deviations from the mean

Probability Calculation

Determining likelihood based on actual or perceived outcomes

Personal Belief

Trust in statistical findings and methodologies

Empirical Frequencies

Observed frequencies in a dataset

Classic Probability

Calculating probabilities based on equally likely outcomes

Conditional Probability

Likelihood of an event given another has occurred

Bayes Rule

Formula updating probability based on new evidence

Standard Error of the Mean

Variability of sample means around the population mean

Central Limit Theorem

Sample mean distribution approaches normal with increasing sample size

Population

Entire group of individuals, items, or events under study

Sample

Subset used to draw conclusions about a larger population

Sample Statistics

Numerical insights into population characteristics from a sample