Lecture 2: Defining Statistics Flashcards
Descriptive statistics
Tools for summarizing, organizing, and simplifying data about a sample
Inferential Statistics
Data from a sample used to draw inferences about a population
Parameters v. Statistics
Parameters:
Characteristics of the population that are unknown and must be inferred
Statistic: Characteristic measured (known) from a sample
Statistics are used to estimate unknown parameters
Sampling error
Discrepancy between sample statistic and population parameter
Precision error: statistical error with respect to either side of the pop. parameter
Bias error: statistical error with respect to pop. parameter biased toward one side of the scale.
Representative sample
Sample whose characteristics are similar to the population
Random sample
each person in population has equal chance of being selected for sample; decreases sampling bias error
Steps to model the real world
- Operationalize the variables
• make the variable (“social media”) something measurable/defined (“minutes per day”) - Identify variable structure
• What type of variable: Discrete (counted)? Continuous (measured)?
Types of variables
Discrete variables can be qualitative or quantitative and can be counted
Continuous variables have an infinite number of values that can fall between any two observed values; Cont. fall within an interval and are approximatively measurable
Levels of measurement
Ratio
Interval
Ordinal
Nominal
Nominal measurement
Set of categories with different names
Unordered
Differences in kind but not quantitative
Ordinal measurement
Nominal +
Ordered categories, represent differences in rank, level, degree
Interval measurement
Ordinal+
Equal size intervals
No true zero points
Ratio measurement
Interval +
True zero point
