Stats Unit 1 Exam

Statistics

involves the tools and techniques that allow us to collect, organize, describe, ananlyze, and ultimatley rpesent an interpretation of data

Raw Data

data that appears in its original form
turns into a statistic after a calculation
Ex: a list of numbers or words

Statistical Interference

where we attempt to make predictions about the effects of our actions in the world, based on data

Data Set

a single location which contains all of the information we gathered on a topic presented in an organized fashion

Structured Data

table, graph, etc. top row displays the varibales or attributed measured during analysis, each row represents data for a single record

Numerical (Quantitative)

variables have a magnitude, are measureable units with values that carry an equal weight
( Discrete and Continuous)

Discrete

variables can only take on a specific value (whole numbers)
Ex: number of siblings, year of birth

Continuous

variables can take on any real number value (can be a decimal)
Ex: height, temperature

Categorical (Qualitative)

variables have values that describe labels or attributes, even if the variables can be placed in a logical order thye have no magnitude or units
(Nomial and Ordinal)

Nomial

variables have categories with no distinct or defined order; or a difference in name only. Also variables with only two categories
Ex: gender, attendence, nationality, yes or no

Ordinal

variables have an inherent or obvious order
Ex: likert scales (strongly agree, agree, neutral, disagree, strongly disagree), class in school (freshman, sophomore, junior, senior)

Population

collection of all possible members or outcomes for groups we are interested in

Parameter

when we complete a calculation on a set of data from a population
Ex: average years of education from all people in the U.S.

Sample

when we take a smaller group from the population that represents the population well

Statistic

calculation derived from samples

Statistical Interference

the process of using a sample statistic to estimate a population parameter.

Simple Random Sampling

Each member of a population has an exactly equal chance of being selected

Stratified Random Sampling

where a population is divided into smaller groups or clusters, and samples are selectedfrom each cluster

Convience Sampling

a researcher only includes people who were convient to obtain a sample from

Measurement Bias

comes from some form of measurement error

Questionaire/ Interviewer Bias

can occur when the way in which a question is asked can alter the particpants responses

Instrument Bias

can occur as a result of systematically measuring responses innaccuratley
Ex: improperly calibrated scale

Sampling Bias

happens when the sample we choose does not reflect the full spectrum of the population

Selection Bias

some individuals within a target population are more liekly to be selected for inclusion than others

Under-or-Over Representation Bias

can occur if we choose to select our sample from a time or place during which certain portions of the population are sampled more or less frequently than they normally would have been

Non-Response Bias

people selected to particpate in the survey do not complete it

Voluntary Response Bias

can happen when sample responses come from self-selected volunteers
Ex: Internet Surveys

Confounding Variable

Variable that offers the results

Bias

a systematic error in sampling

Random Sample

representative group from the population