Chapter 1 Vocab and Concepts
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| Variable | a characteristic or attribute that can assume different values
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| Data | the values (measurements or observations) that the variables can assume
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| Random Variables | Variables whose values are determined by chance
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| Data Set | A collection of data values
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| Data Value (Datum) | Each value in a data set
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| Statistics | the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data
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| Descriptive Statistics | consists of the collection, organization, summarization, and presentation of data
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| Inferential Statistics | consists of generalizing from samples to populations, performing estimations and hypothesis tests, determining relationships among variables, and making predictions
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| Probability | the chance of an event occurring
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| Population | consists of all subjects that are being studied
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| Sample | a group of subjects selected from a population
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| Hypothesis Testing | a decision-making process for evaluating claims about a population based on information obtained from samples
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| Qualitative Variables | variables that can be placed into distinct categories, according to some characteristic or attribute
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| Quantitative Variables | numerical variables that can be ordered or ranked
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| Discrete Variables | variables which assume values that can be counted
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| Continuous Variables | variables which can assume an infinite number of values between any two specific values. Obtained by measuring and often include fractions and decimals
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| Measurement Scales | four common types of these scales are nominal, ordinal, interval, and ratio
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| Nominal Level of Measurement | classifies data into mutually exclusive (nonoverlapping), exhausting categories in which no order or ranking can be imposed on the data
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| Ordinal Level of Measurement | classifies data into categories that can be ranked; however, precise differences between the ranks do not exist
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| Interval Level of Measurement | ranks data, and precise differences between units of measure do exist; however there is no meaningful zero
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| Ratio Level of Measurement | possesses all the characteristics of interval measurement, and there exists a true zero. In addition, true ratios exist when the same variable is measured on two different members of the population
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| Four Basic Sampling Techniques | Random, Systematic, Stratified, & Cluster
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| Random Samples | selected by using chance methods or random numbers
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| Systematic Samples | obtained through numbering each subject of the population and then selecting every nth subject
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| Stratified Samples | obtained by dividing the population into groups (called strata) according to some characteristic that is important to the study
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| Cluster Samples | the population is divided into groups called clusters by some means such as geographic area or schools in a large school district, etc.
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| Convenience Sample | uses subjects that are convenient
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| Observational Study | the researcher merely observes what is happening or what has happened in the past and tries to draw conclusions based on these observations
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| Experimental Study | the researcher manipulates one of the variables and tries to determine how the manipulation influences other variables
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| Quasi-Experimental Study | when researchers use already intact groups if random assignment is not possible
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| Independent (Explanatory) Variable | the variable being manipulated in an experimental study
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| Dependent (Outcome) Variable | the resultant variable of an experimental study
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| Treatment Group | the group that receives specific treatment in an experimental study
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| Control Group | the group that receives no treatment in an experimental study
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| Hawthorne Effect | discovered in 1924 when researchers found that the subjects who knew they were participating in an experimental study actually changed their behavior in ways that affected its results
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| Confounding Variable | variable that influences the dependent/outcome variable but was not separated from the independent variable
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| Define statistics | the science of conducting studies to collect, organize, summarize, analyze, and draw conclusions from data
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| Three examples of how statistics is used in everyday life. | 1) Fields of human endeavor (2) Analyze results of a survey (3) Tool in scientific research to make decisions based on controlled experiments (4) Operations research, quality control estimation, & predictions
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| 3 reasons to study statistics | (1) Understand statistical studies (2) Conduct research, design experiments, make predictions, and communicate results (3) Become a better consumer
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| Branch areas of statistics | Differential and Inferential. Differential statistics deals with the collection, organization, summarization, and presentation of data, whereas inferential statistics deals with generalizing from samples to populations and making predictions/inferences.
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| Examples of Variables | Qualitative - gender, eye color, etc. Quantitative - age, height, weight. Discrete - #'s of something. Continuous - often decimals obtained by measuring.
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| Examples of each of the levels of measurement | Nominal - Gender, Zip code, Eye color
Ordinal - Competition rankings, a person's build, letter grades
Interval -
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