Stats chapter 1 and 2

Terms
Definitions/ Info
Random Sample
Draw a sample from a larger population to represent the whole population. Put names in a hat. Make sure every member/element has equal chance of being selected.
Randomly Assign
1/2 of random sample goes to treatment group and the other 1/2 goes to the control group.
Population
entire collection of events (scores, incomes, speeds, etc.) that you’re studying
Sample
small number from larger population. Allows us to infer something about characteristics of population.
External Validity
1st aspect of randomness. Does the sample reflect the population? Ex. Small town from NE wouldn’t rep US hispanic culture well.
Random Assignment
2nd aspect of randomness. After the subjects have been selected, subjects then have to be randomly assigned to treatment or control.
Internal Validity
Are the results the result of the differences in the way we treated our groups (hope so) and not a result of WHO we placed in each group.
Variable
property of an object/event that can take on different values (hair color, self-confidence, gender, personal control, treatment groups)
Independent Variable
Researchers decide what these will be (group memberships like gender groups or teaching style, etc.)
Dependent Variable
Researchers have NO control over these. (resulting self-esteem scores, personal control, etc.)
Discrete Variables
limited number of values (gender, high school grades)
Continuous Variables
any value between lowest and highest points on a scale (age, self-esteem)
Quantitative Data
aka. Measurement Data.Numerical data (weights, test scores) other “how much” tests
Measurement Data
aka. Quantitative Data. Numerical data (weights, test scores) other “how much” tests
Categorical Data
aka. Qualitative/Frequency Data. (no numbers)
Frequency Data
aka. Qualitative/Categorical Data. (no numbers)
Qualitative Data
aka. Categorical/Frequency Data. (no numbers)
Descriptive Statistics
Describing a set of data (means, graphs, extreme scores, oddly shaped distributions)
Exploratory Data Analysis (EDA)
Joh Tukey showed necessity of paying close attention to examining data in close detail before invoking more tech involved procedures
Inferential Statistics
do after descriptive statistics, after we have a basic understanding of the numbers.
Parameter
a measure that refers to an entire population (average self-esteem score)
Statistic
same measure as a parameter, calculated from sample of data we have collected.
Nominal Scale
labels categorical data. (gender, political parties)
Ordinal Scale
simplest true scale. Orders people, objects or events along a continuum (ranks in military)
Interval Scale
allows us to speak of differences between scale points (same different between 10-15 degree C as there is between 15-20 degree C)
Ratio Scale
has a true 0 point (true absence). Allows us to speak of ratios/fractions. (length, volume, time)

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