📚 STAT 100 — Chapters 1–3 Flash Cards
FLASH CARD 1
Front: What is an estimate?
Back: A number or percentage used to describe something about a population.
Example: 75% of students play a sport → 75% = estimate
FLASH CARD 2
Front: What is the population of interest?
Back: The whole group of people or things you want information about.
Example: 75% of high school students play a sport → high school students = population.
FLASH CARD 3
Front: What is an attribute?
Back: A characteristic or piece of information being measured about an observation.
Example: Name, major, GPA, age.
FLASH CARD 4
Front: What is an observation?
Back: One person, object, place, or event in a dataset.
⭐ Usually, one row = one observation.
FLASH CARD 5
Front: In a tidy data table, what does each ROW represent?
Back: One observation.
FLASH CARD 6
Front: In a tidy data table, what does each COLUMN represent?
Back: One attribute.
FLASH CARD 7
Front: What is quantitative data?
Back: Data that represents a number or amount.
Examples: GPA, age, price, height, wind speed.
FLASH CARD 8
Front: What is categorical data?
Back: Data that places something into a group or category.
Examples: major, eye color, blood type, ocean.
FLASH CARD 9
Front: Is blood type quantitative or categorical?
Back: Categorical. A, B, AB, and O are categories, not amounts.
FLASH CARD 10
Front: What is a rating scale?
Back: Categories that have a meaningful order.
Example: Poor → Fair → Good → Excellent.
FLASH CARD 11
Front: What is text data?
Back: Written words or responses.
Example: A student writing why they enjoyed a class.
FLASH CARD 12
Front: What is time-series data?
Back: Data collected or measured over time.
Example: Google’s stock price each day.
FLASH CARD 13
Front: What is a measurement?
Back: The way an attribute is measured or collected.
FLASH CARD 14
Front: What is reliability?
Back: How much you can trust a measurement to give dependable information.
FLASH CARD 15
Front: What is data validation?
Back: Rules used to prevent invalid data from being entered.
Example: If GPA must be 0–4, don’t allow someone to enter 40.
FLASH CARD 16
Front: What is data cleaning?
Back: Finding and handling invalid or incorrect values AFTER data has been collected.
FLASH CARD 17
Front: Validation vs. data cleaning?
Back:
Validation = PREVENT bad data.
Cleaning = deal with bad data afterward.
FLASH CARD 18
Front: What is a generalization claim?
Back: Using information from a sample to make a claim about a larger population.
FLASH CARD 19
Front: What is a causal claim?
Back: A claim saying one thing causes another thing to change.
FLASH CARD 20
Front: What is a causal factor?
Back: The thing believed to cause the change.
Example: “Eating breakfast improves grades.” → Eating breakfast = causal factor.
FLASH CARD 21
Front: What is an outcome?
Back: The thing that is affected or changed.
Example: “Eating breakfast improves grades.” → Grades = outcome.
⭐ QUICK MEMORY CARD
Front: What should I remember most for Chapters 1–3?
Back:
Estimate = NUMBER
Population = WHO
Attribute = WHAT
Observation = ROW
Attribute = COLUMN
Quantitative = AMOUNT
Categorical = GROUP
Time series = OVER TIME
Reliability = CAN I TRUST IT?
Validation = PREVENT bad data
Cleaning = FIX/REMOVE bad data afterward
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