Ch 1-6 Intro to Stats

Question 1

Manipulated variable by the researcher. Has different experimental conditions

Answer 1

Independent variable

Question 2

Measured variable by researcher as it naturally responds to other factors

Answer 2

Dependent variable

Question 3

Typically a range of techniques and procedures that are used to analyze, interpret, display, or make decisions based on data.

Answer 3

Statistics

Question 4

Represents the measured value of variables

Answer 4

Data

Question 5

Characteristic/feature of the subject/item that we are interested in understanding.

Answer 5

Variable

Question 6

Variables that express an attribute that do not imply a numerical ordering. Ex: hair color, eye color, religion, gender, etc.

Answer 6

Qualitative variables (categorical)

Question 7

Variables that are measured in terms of numbers. Ex: height, weight, grip strength, levels of testosterone

Answer 7

Quantitative variables (numerical)

Question 8

Specific values that cannot be subdivided. They have no decimals, but the averages of them can be factorial. Ex: number of siblings

Answer 8

Discrete (quantitative) variables

Question 9

Can be meaningfully split into smaller parts. They are generally measured using a scale. Ex: time to respond

Answer 9

Continuous (quantitative) variables

Question 10

Categorizes variables into mutually exclusive labeled categories (not in rank order). Ex: gender categories- male, female, nonbinary, transgender, other

Answer 10

Nominal scales

Question 11

Classifies variables into categories that have a natural order or rank. Ex: Strongly agree, somewhat agree, neither agree nor disagree, somewhat disagree, strongly disagree

Answer 11

Ordinal scales

Question 12

Measures variables on a numerical scale that has equal intervals between adjacent values. There is NO true zero (not a complete absence of something) Ex: Temperature (zero doesn’t mean absence of heat)

Answer 12

Interval scales

Question 13

Interval scales but with a true zero. Ex: You can answer “0” on a question that asks how many children you have.

Answer 13

Ratio scales

Question 14

A specified group that a researcher is interested in. Can be really broad or narrow. Ex: “All people” or “all psychology students at CSUF”

Answer 14

Population

Question 15

subset of a population Ex: 50 out of 5000 people

Answer 15

Sample

Question 16

Conclusions that are only applicable to a sample but not the general population

Answer 16

Sampling bias

Question 17

every member of the population has an equal chance of being selected into the sample. It is completely random. Ex: Using a random number generator to pick participants.

Answer 17

Simple random sampling (SRS)

Question 18

Identify members of each group, then randomly sample within subgroups. Ex: Dividing members based on their ethnic backgrounds. If there’s more people in a certain subgroup, there might be more people of that subgroup in the population.

Answer 18

Stratified sampling

Question 19

Picking a sample that is close at hand. Ex: TitanWalk booths just pick a random student that walks by closest to their booth.

Answer 19

Convenience sampling

Question 20

Ex: Low GPA is associated with low levels of sleep

Answer 20

Associable claims

Question 21

Ex: The more caffeine you take, the more hyperactive you get.

Answer 21

Casual claims

Question 22

Involves manipulation of an independent variable, identifiable when a research has different conditions (different IV levels). Supports causal claims. (that X caused Y)

Answer 22

Experimental design

Question 23

Involves manipulation of an independent variable but DOESN’T USE random assignment. Can somewhat support causal claims. (It is very likely X caused Y)

Answer 23

Quasi-experimental designs

Question 24

Involves observing things as they occur naturally and recording observations. Supports association claims, also called correlation research.

Answer 24

Non-experimental designs

Question 25

Use dot summarize and DESCRIBE data from a sample. You can’t generalize things with these types of statistics. Ex: mean, median, mode, frequencies, range, etc.

Answer 25

Descriptive statistics

Question 26

Used to generalize the data from our sample to the population or to other samples. These types of statistics are used to DRAW CONCLUSIONS. Ex: t tests, regression, ANOVA, etc.

Answer 26

Inferential statistics

Question 27

Percentage Formula

Answer 27

Relative Frequency x 100

Question 28

Relative Frequency Formula

Answer 28

Frequency/total

Question 29

thin part (fewer scores) to the right

Answer 29

Positively skewed

Question 30

thin part (fewer scores) to the left

Answer 30

Negatively skewed

Question 31

number describing a sample Ex: X-bar = 3,800

Answer 31

Statistic

Question 32

number describing a whole population. Ex: μ = 4,000

Answer 32

Parameter

Question 33

Utilizing terms to make a graph universally understood. Ex: Using one of the most representable breeds of a mixed dog to describe what a dog looks like. Three features: form, central tendency, and variability

Answer 33

distributions

Question 34

A value that attempts to describe a whole set of data with a single value that represents a distribution’s middle/center. Allows us to describe our sample ONLY
DOESN’T allow us to make general conclusions about a broader population/confirm differences between other samples

Answer 34

Central tendency

Question 35

Arithmetic average, sum of the scores divided by the number of scores

Answer 35

Mean

Question 36

Point that divides a distribution of scores into equal halves

Answer 36

Median

Question 37

Score that occurs most frequently in a distribution

Answer 37

Mode

Question 38

Mean, median, & mode are the same

Answer 38

Symmetric distribution

Question 39

Mean, median, & mode are NOT the same

Answer 39

Skewed distribution

Question 40

Mean is smaller than median

Answer 40

Negative skew

Question 41

Mean is larger than median

Answer 41

Positive skew

Question 42

Refers to how spread out a group of scores are. Used synonymously with spread and dispersion.

Answer 42

Variability

Question 43

75th percentile - 25th percentile. Range of scores that contains the middle 50% of a distribution.

Answer 43

Interquartile Range

Question 44

Descriptive measure of the dispersion of scores around the mean

Answer 44

Standard deviation

Question 45

A bell-shaped, theoretical distribution that predicts the frequency of occurrence of chance events.

Answer 45

Normal distribution

Question 46

Hypothesized scores based on mathematical formulas and logic

Answer 46

Theoretical distributions

Question 47

A distribution that comes from direct observations

Answer 47

Empirical distribution

Question 48

A standardized version of a raw score (X) that gives information about the relative location of that score within its distribution

Answer 48

Z-score

Question 49

Tells you the distance of the score between the center and the mean

Answer 49

magnitude

Question 50

Sample statistics tend to differ from true population values.

Answer 50

Sampling error

Question 51

Probability of an event (A). Ranges from .00 (no possibility to 1.00 (guaranteed to happen. These can be converted into percentages by multiplying by 100. Ex: The probability of getting heads by flipping a coin. P(Heads) = ½ = .50 = 50%

Answer 51

Probability: P(A)

Question 52

probability distributions created by drawing many random samples of a given size (n) from the same population for a given statistic.

Answer 52

sampling distribution

Question 53

When we take a sample from a population and interpret the data of that sample.

Answer 53

Sample distribution

Question 54

“For any population of scores, regardless of form, the sampling distribution of the mean approaches a normal distribution as N (sample size) gets larger. Furthermore, the sampling distribution of the mean has a mean equal to μ and a standard deviation (standard error) equal to 2/n.

Answer 54

Central Limit Theorem