Exam

Question 1

Simple random sampling

Answer 1

Everyone has an equal chance.
Example: Pulling names out of a hat.

Question 2

Stratified sample

Answer 2

Split into groups (strata), take a few from each.
Example: Split by grade level, randomly choose 5 students from each.

Question 3

Cluster sample

Answer 3

Split into groups (clusters), pick a few whole groups.
Example: Randomly pick 2 classrooms and survey everyone in them.

Question 4

Systematic sample

Answer 4

Pick every nth person.
Example: Every 5th person on a list

Question 5

Connvience sample

Answer 5

Ask whoever’s easy to reach.
Example: Surveying friends in the cafeteria. (This is bad—leads to bias!)

Question 6

Voluntary response set up

Answer 6

People choose to respond.
Example: An online poll where only those who care respond. (Also bad—very biased

Question 7

Bias

Answer 7

When the sample doesn’t fairly represent the population.

Question 8

Under coverage

Answer 8

Some people have no chance to be chosen.

Question 9

Non response

Answer 9

People chosen for the sample don’t respond.

Question 10

Wording bias

Answer 10

The way a question is asked affects answers.

Question 11

Observational study

Answer 11

You just watch; you don’t change anything.

Question 12

Experiment

Answer 12

You do something (a treatment) to see the effect

Question 13

Subject

Answer 13

The person or thing being studied

Question 14

Treatment

Answer 14

What’s done to the subject in an experiment.

Question 15

Factor

Answer 15

The variable being changed in an experiment.

Question 16

Level

Answer 16

The specific values of a factor.

Question 17

Control

Answer 17

Keep variables the same so results are fair.

Question 18

Random assignment

Answer 18

Randomly put subjects into groups.

Question 19

Replication

Answer 19

Use enough subjects so results are reliable.

Question 20

Comparison

Answer 20

Compare different groups or treatments.

Question 21

Placebo

Answer 21

A fake treatment

Question 22

Blinding

Answer 22

Keeping subjects (or researchers) from knowing who got what

Question 23

Single blind

Answer 23

Subjects OR researchers don’t know the treatment

Question 24

Double blind

Answer 24

Neither subjects nor researchers know the treatment

Question 25

Blocking

Answer 25

blocking is an experimental design technique used to reduce variability by accounting for known differences among subjects.

Question 26

Confounding variable

Answer 26

A hidden factor that messes up the results.

Question 27

Scope of inference

Answer 27

What you’re allowed to say based on how the data was collected

Question 28

Type 1

Answer 28

False positive

Question 29

Type 2

Answer 29

False negative

Question 30

Simulation

Answer 30

A simulation is a method used to model real-world randomness using tools like dice, spinners, random number generators, or even drawing slips of paper.

Question 31

Binomial distribution

Answer 31

A fixed number of trials (n) • Only two outcomes per trial (success/failure) • The probability of success (p) stays the same • Trials are independent (the result of one doesn’t affect another) The acronym BINS is often used to remember this: • Binary • Independent • Number of trials is fixed • Same probability

Question 32

Geo

Answer 32

1/p

Question 33

Binomial distribution

Answer 33

Used for: A set number of repeated yes/no situations • Key idea: You’re counting how many times you get a success • Example: Flipping a coin 10 times and counting how many heads you get

Question 34

Geo dist

Answer 34

Used for: Repeating a yes/no situation until you get your first success • Key idea: You’re counting how many tries it takes to get a success • Example: Flipping a coin until you get the first heads.

Question 35

Unifrom distribution

Answer 35

Used for: When all outcomes are equally likely • Looks like: A flat, even graph • Example: Rolling a fair die — each number (1 through 6) has the same chance.

Question 36

Natural distribution

Answer 36

Normal Distribution • Looks like: A smooth, bell-shaped curve • Used for: Data that’s naturally centered around an average (like height or test scores) • Example: Most people are average height, fewer are really short or really tall

Question 37

CSOCS distribution of quantitative data

Answer 37

Context Shape symmetrical or skewerd Outlier Center Spread range sd IQR

Question 38

Explain scatter plots using CDOFS

Answer 38

Context Direction Outliers Form Strength

Question 39

Explain how to calculate a residual

Answer 39

Y - Y^

Question 40

Residual

Answer 40

Degree of error of regression line prediction