Qualitative Data
Consist of labels or descriptions of traits of the sample, also known as categorical data. Ex: identification numbers.
Quantitative Data
Consist of counts or measurements and therefore, are numerical. Ex: test scores, median weights.
Continuous Data
Data that is measured. Ex: temperature.
Discrete Data
Data that is counted. Ex: number of marbles in a bag.
Nominal (math doesn't apply)
Data that represent whether a variable possesses some characteristic. Ex: identification numbers.
Ordinal (math doesn't apply)
Data that represent categories that have some associated order. Ex: 1st, 2nd, 3rd in a race.
Interval (math applies)
Data that can be ordered and the arithmetic difference is meaningful. Ex: Temperature scale. NOTE: with interval data, zero does not mean the absence of something.
Ratio (math applies)
Ratio data are similar to interval data, except that they have a meaningful zero point and the ratio of two data points is meaningful. Ex: cost of bread at a store. NOTE: the cost of one loaf of bread can be a certain percentage higher or lower than anoth
Conducting a Statistical Study
1) Determine the question, population, variables, and sampling method.
2) Collect data
3) Organize data
4) Analyze data to answer the question
Observational Study
Observes data that already exists.
Experiement
Generates data to help identify cause-and-effect relationships.
Representative Sample
Has the same relevant characteristics as the population and does not favor one group from the population over another.
Random Sample
Sample in which every member of the population has an equal chance of being selected. Ex: drawing names from a hat.
Simple Randmom Sample
Each possible sample has an equal chance of being selected. Ex: Select 25 jellybeans randomly from a jar. In this case, each particular combination of 25 jellybeans has an equal chance of being selected.
Stratified Sampling
Members of the population are divided into two or more subgroups, called strata, that share similar characteristics. A random sample from each stratum is then drawn.
Cluster Sampling
Divide the entire population into pre-exsisted segments or clusters. The clusters are often geographic. Make a random selection of clusters. Include every member of each selected cluster in the sample
Systematic Sampling
Choose every nth member of the population. Ex: choose every 3rd student in a class.
Convenience Sampling
Sample is convenient to select. Ex: Survey.
Cross-Sectional Study
Data are collected at a single point in time.
Longitudinal Study
Data are gathered by following a particular group over a period of time.
Meta-Analysis
Study that compiles information from previous studies.
Case Study
Looks at multiple variables that affect a single event.
Treatment
Some condition that is applied to a group of subjects in an experiment.
Subjects
People or things being studied in an experiment. If people, they are called "participants".
Response Variable
The variable in an experiment that responds to the treatment.
Explanatory Variable
The variable in an experiment that causes the change in the response variable.
Principles of Experimental Design
1) Randomize the control and treatment groups.
2) Control for outside effects on the response variable.
3) Replicate the experiment a significant number of times to see meaningful patterns.
Treatment Group
Group of subjects to which researchers apply a treatment in an experiment.
Control Group
Group of subjects to which no treatment is applied in an experiment.
Cofounding Variables
Factors other than the treatment that cause an effect on the subjects of an experiment.
Placebo Effect
Response to the power of suggestion, rather than the treatment itself, by the participants of an experiment.
Placebo
Substance that appears identical to the actual treatment but contains no intrinsic beneficial elements.
Single-Blind
Experiment in which the subjects do not know if they are in the control group or the treatment group, but the people interacting with the subjects in the experiment know in which group each subject has been placed.
Double-Blind
Experiment in which neither the subjects nor the people interacting with the subjects know in which group each subject belongs.
Institutional Review Board
Group of people who review the design of a study to make sure that it is appropriate and that no unnecessary harm will come to the subjects involved.
Informed Consent
Completely disclosing to participants the goals and procedures involved in a study and obtaining their agreement to participate.
Bias
Favoring of a certain outcome in a study.
Sampling Bias
Sample chosen does not accurately represent the population being studied.
Dropouts
Participants who begin a study but fail to complete it. (Can reduce the size of a sample, thus affecting how representative your sample is of the population.)
Processing Errors
Errors that occur simply from the data being processed, such as typos when data are being entered.
nonadherents
Participants who remain in the study until the end but stray from the directions they were given.
Researcher Bias
Researcher influences the results of a study.
Response Bias
Researcher's behavior causes a participant to alter his or her response or when a participant gives an inaccurate response.
Participation Bias
Problem with either the participation�or lack thereof�of those chosen for the study.
Nonresponse Bias
Lack of participation in a self-selected sample from certain segments of a population, when a person refuses to participate in a survey, or when a respondent omits questions when answering a survey.
descriptive statistics
Numerical characterizations that describe data.
inferential statistics
A set of procedures used to make judgements about whether differences actually exist between sets of numbers