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Statistics, Data, and Statistical Thinking Flashcards

1.1 The Science of Statistics
1.2 Types of Statistical Applications
1.3 Fundamental Elements of Statistics
1.4 Types of Data
1.5 Collecting Data
1.6 The Role of Statistics in Critical Thinking

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9928343StatisticsThe science of data. This involves collecting, classifying, summarizing, organizing, analyzing, and interpreting numerical information.0
9928344Descriptive statisticsUtilizes numerical and graphical methods to look for patterns in a data set, to summarize the information revealed in a data set, and to present that information in a convenient form.1
9928345Inferential statisticsUtilizes sample data to make estimates, decisions, predictions, or other generalizations about a larger set of data.2
9928346PopulationA set of units (usually people, objects, transactions, or events) that we are interested in studying.3
9928347VariableA characteristic or property of an individual population unit.4
9928348MeasurementThe process we use to assign numbers to variables of individual population units.5
9928349CensusUsed to measure a variable for every unit of a population.6
9928350SampleA sample is a subset of the units of a population.7
9928351Statistical inferenceAn estimate, prediction, or some other generalization about a population based on information contained in a sample. That is, we use the information contained in the smaller sample to learn about the larger population.8
9928352ReliabilityHow good the inference is.9
9928353Resource constraintsConstraining factors (insufficient time and / or money) that affect a statistical method of data collection.10
9928354Uncertainty (Degree of)The element introduced when a measure of reliability is not used.11
9928355Bound on the estimation errorA number that our estimation error is not likely to exceed12
9928356Measure of reliabilityA statement (usually quantified) about the degree of uncertainty associated with a statistical inference.13
9928357Four Elements of Descriptive Statistical Problems1. The population or sample of interest; 2. One or more variables (characteristics of the population or sample units) that are to be investigated; 3. Tables, graphs, or numerical summary tools; 4. Identification of patterns in the data.14
9928358Five Elements of Inferential Statistical Problems1. The population of interest; 2. One or more variables that are to be investigated; 3. The sample of population units; 4. The inference about the population based on information contained in the sample; 5. A measure of reliability for the inference.15
9928359Quantitative dataMeasurements that are recorded on a naturally occurring numerical scale.16
9928360Qualitative dataMeasurements that cannot be measured on a natural numerical scale; they can only be classified into one of a group of categories.17
9928361Published sourceThe data set of interest has already been collected for you and is available.18
9930352Designed experimentA method of collecting data that involves a ______ in which the researcher exerts strict control over the units in the study.19
9930353SurveyThe researcher samples a group of people, asks one or more questions, and records the responses. _____ can be conducted through the mail, with telepone interviews, or with in-person interviews. Although in-person _____ are more expensive than mail or telephone surveys, they may be necessary when complex information is to be collected.20
9930354Observational studyThe researcher observes the experimental units in their natural setting and records the variable(s) of interest. The researcher makes no attempt to control any aspect of the experimental units.21
9930355Representative sampleExhibits characteristics typical of those possessed by the target population.22
9930356Random sampleEnsures that every subset of fixed size in the population has the same chance of being included in the sample.23
9930357ParametersStatistical measures that are computed regarding the characteristics of a population.24
9930358Sample designRefers to the technique employed to select a subset of participants from the population and gather the data from the population.25
9930359Voluntary responseA very common design employed particularly in opinion surveys. In this design, a general appeal is made for responses to one or more questions. Members of the population decide for themselves whether or not to respond. It is likely that only very motivated listeners will respond.26
9930360Convenience samplingIn this design, members of the population are chosen based on the convenience of including them.27
9930361Quota samplingIn this procedure, interviewers are assigned to interview a fixed amount of members of the population. These amounts are organized around categories such as race, gender, residence, or economic status; in many cases, the amounts are set to match known or assumed demographic information about the population.28
9930362Simple random sampling (SRS)Several important features: 1. Involves selecting individuals at random from the population without replacement; 2. a sample of size n is to be chosen from the population, where every population subset of size n has an equal chance of being selected; 3. every member of the population has an equal chance of being included in the sample.29
9930363BiasA systematic error that favors a particular segment of the population or that tends to encourage only certain outcomes in the data.30
9930364Stratified random sampleIf a population has distinct groups, it is possible to divide the population into these groups and draw SRS's from each of the groups. The groups are called strata. Strata are designed so that members in each strata are more homogeneous, that is, more similar to each other. The results of all of the SRS's are then grouped together to form the sample. This technique is particularly useful in populations that can be stratified into groups by gender, race, or geography (such as urban, rural, and suburban) when the researcher wants to ensure representation from each group.31
9930365Multi-stage cluster sampleThe process involves taking stages and SRS's within a cluster. While this multi-stage technique sounds very complicated, it is atually quite efficient and cost-effective.32
9930366Systematic sampleThis method of sampling begins with the listing of the population. Then, a decision is made as to a systematic way of choosing members. For example, if it is decided to sample 1 of every 50, the investigator would randomly select one of the first 50 and then continue selecting every 50th member from that point on. In this way, if member 12 was selected first, then members 62, 112, 162, and so on would be selected. For systematic sampling to be valid, the investigator must make sure that the ordering principle is not connected to the nature of the population.33
9931081Valid probability methods of sampling1. The interviewers and subjects themselves are not choosing the subject who is interviewed; 2. There is a definite procedure for selecting participants in the sample and that procedure involves the use of probability.34
9931082Sampling frameThe list of possible subjects who could be selected in a sample.35
9931083Response bias1. The wording of the questions; 2. The order of choices; 3. The deamenor and / or appearance of the interviewer; 4. Honesty36
9931084Non-response biasWhen members of the population are chosen but cannot be contqacted to participate, non-response bias may occur. This is true since non-respondents tend to to differ from those who are readily available. Efforts are made to reduce this as much as possible.37
9931085Household biasAnother type of bias involving the power of smaller households in relation to larger ones38
9931086Random sampling errorError that occurs because of chance variation39
9931087Sampling method errorError that occurs because of the choice of sampling method.40
9931088Non-sampling method errorError that occurs in the responses by members in the sample.41
9931089Comparative experimentA type of experimental design that involves two or more groups.42
9931090Independent (explanation) variablesExplanation43
9931091Dependent (response) variablesResponse44
9931092Control groupConsists of the units who are not to receive the treatment that is the focus of the experiment45
9931093Treatment groupUnits in this group receive the treatment.46
9931094Confounded variablesTwo variables are _______ if the investigator cannot separately identify their effects on the response variable.47
9931095Lurking variablesA variable that has an effect on the response variable but is not measured as part of the study of interest.48
9931096FactorsAnother name for explanatory variables.49
9931097LevelsA treatment is a combination of specific values of each of the factors; these values are called _______.50
9931098Statistical thinkingInvolves applying rational thought to assess data and the inferences made from them critically.51

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