## 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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