Intro Statistics - Lesson 1

inferential statistics

A set of procedures used to make judgements about whether differences actually exist between sets of numbers

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

Quantitative Data

Consist of counts or measurements and therefore, are numerical. Ex: test scores, median weights.

Qualitative Data

Consist of labels or descriptions of traits of the sample, also known as categorical data. Ex: identification numbers.

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

Double-Blind

Experiment in which neither the subjects nor the people interacting with the subjects know in which group each subject belongs.

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.

Representative Sample

Has the same relevant characteristics as the population and does not favor one group from the population over another.

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.

Placebo Effect

Response to the power of suggestion, rather than the treatment itself, by the participants of an experiment.

Case Study

Looks at multiple variables that affect a single event.

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.)

nonadherents

Participants who remain in the study until the end but stray from the directions they were given.

Subjects

People or things being studied in an experiment. If people, they are called "participants".

Explanatory Variable

The variable in an experiment that causes the change in the response variable.

Response Variable

The variable in an experiment that responds to the treatment.

Experiement

Generates data to help identify cause-and-effect relationships.

Meta-Analysis

Study that compiles information from previous studies.

Placebo

Substance that appears identical to the actual treatment but contains no intrinsic beneficial elements.

Bias

Favoring of a certain outcome in a study.

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 another loaf of bread, unlike with temperatures (interval data.)

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.

Systematic Sampling

Choose every nth member of the population. Ex: choose every 3rd student in a class.

Informed Consent

Completely disclosing to participants the goals and procedures involved in a study and obtaining their agreement to participate.

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.

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.

Discrete Data

Data that is counted. Ex: number of marbles in a bag.

Continuous Data

Data that is measured. Ex: temperature.

Ordinal (math doesn't apply)

Data that represent categories that have some associated order. Ex: 1st, 2nd, 3rd in a race.

Nominal (math doesn't apply)

Data that represent whether a variable possesses some characteristic. Ex: identification numbers.

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.

Processing Errors

Errors that occur simply from the data being processed, such as typos when data are being entered.

Cofounding Variables

Factors other than the treatment that cause an effect on the subjects of an experiment.

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.

Control Group

Group of subjects to which no treatment is applied in an experiment.

Treatment Group

Group of subjects to which researchers apply a treatment in an experiment.

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.

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.

descriptive statistics

Numerical characterizations that describe data.

Observational Study

Observes data that already exists.

Sampling Bias

Sample chosen does not accurately represent the population being studied.

Random Sample

Sample in which every member of the population has an equal chance of being selected. Ex: drawing names from a hat.

Convenience Sampling

Sample is convenient to select. Ex: Survey.

Treatment

Some condition that is applied to a group of subjects in an experiment.