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41 Cards in this Set
- Front
- Back
Data
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Collections of observations, such as measurements, genders, or survey responses
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Statistics
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The science of planning studies and experiments, obtaining data, and then organizing, summarizing, presenting, analyzing, interpreting, and drawing conclusions based on the data.
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Population
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The complete collection of all measurements of data that are being considered.
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Census
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Collection of data from every member of a population.
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Sample
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Sub-collection of members selected from a population.
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Sampling Method:
Voluntary response (or self-selected) |
samples often have bias (those with special interest are more likely to participate).
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Statistical significance
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is achieved in a study when we get a result that is very unlikely to occur by chance.
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Practical significance
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Common sense might suggest that the finding does not make enough of a difference to justify its use of its use or to be practical.
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Potential Pitfalls
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Misleading conclusions
Too small of a sample used Loaded questions were used The order of the questions Nonresponse Missing Data Precise Numbers Percentages |
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Parameter
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a numerical measurement describing some characteristic of a population.
Population = Parameter |
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Statistic
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a numerical measurement describing some characteristic of a sample
Sample = Statsistic |
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Quantitative Data
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(or numerical data)
Consists of numbers representing counts or measurements. Ex: Weights, Ages |
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Categorical Data
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(or qualitative data)
Consists of names or labels (representing categories) Ex: gender, Shirt numbers on professional athletes uniforms |
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Quantitative Data
Discrete Data |
results when the number of possible values is either a finite number or a 'countable' number
Ex: 0, 1, 2, 3, ... The number of eggs that a hen lays |
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Quantitative Data
Continuous (numerical) Data |
results from infinitely many possible values that correspond to some continuous scale that covers a range of values without gaps, interruptions, or jumps.
Ex: The amount of milk that a cow produces; e.g. 2.343115 gallons per day. |
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Nominal Level
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characterized by data that consist of names, labels, or categories only, and the data cannot be arranged in an ordering scheme (such as low to high).
Ex: Survey responses yes, no, undecided. |
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Ordinal Level
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involves data that can be arranged in some order, but differences between data values either cannot be determined or are meaningless.
Ex: Course grades A, B, C etc. |
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Interval Level
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involves data that can be arranged in order and the difference between any two data values is meaningful. However, there is no natural zero starting point (where none of the quantity is present).
Ex: Years 1000. 2000, 1776 etc. |
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Ratio Level
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the interval level with the additional property that there is also a natural zero starting point (where zero indicates that none of the quantity is present); for values at the level, differences and ratios are meaningful.
Ex: Prices of college textbooks ($0 represents no cost, a $100 book costs twice as much as a $50 book) |
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Observational study
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observing and measuring specific characteristics without attempting to modify the subjects being studied.
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Experiment
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apply some treatment and then observe its effects on the subjects.
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Simple Random Sample
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a sample of n subjects is selected in such a way that every possible sample of the same size n has the same chance of being chosen.
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Random Sample
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members from the population are selected in such a way that each individual member in the population has an equal chance of being selected.
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Systematic Sampling
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select some starting point and then select every kth element in the population.
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Convenience Sampling
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Using results that are easy to get.
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Stratified Sampling
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Subdivide the population into at least two different subgroups that share the same characteristics, then draw a sample from each subgroup.
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Cluster Sampling
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Divide the population area into sections (or clusters). Then randomly select some of those clusters. Now choose all members from selected clusters.
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Multistage Sampling
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Collect data by using some combination of the basic sampling methods.
In a multistage sample design, pollsters select a sample in different stages, and each stage might use different methods of sampling. |
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Cross-sectional study
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Data are observed, measured, and collected at one point in time.
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Retrospective (or case control) study
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Data are collected from the past by going back in time.
Ex: examine records, interviews |
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Prospective (or longitudinal or cohort) study
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Data are collected in the future from groups sharing common factors (called cohorts).
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Randomization
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is used when subjects are assigned to different groups through a process of random selection. The logic is to use chance as a way to create two groups that are similar.
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Replication
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is the repetition of an experiment on more than one subject.
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Blinding
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is a technique in which the subject doesn't know whether he or she is receiving a treatment or a placebo.
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Double-Blind
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1) The subject doesn't know whether he or she is receiving the treatment or a placebo.
2) The experimenter does not know whether he or she is administering the treatment or placebo. |
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Confounding
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occurs in an experiment when the experimenter is not able to distinguish between the effects of different factors.
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Randomized Block Design
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a block is a group of subjects that are similar, but blocks differ in ways that might affect the outcome of the experiment.
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Matched Pairs Design
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compare exactly two treatment groups using subjects matched in pairs that are somehow related or have similar characteristics.
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Rigorously Controlled Design
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carefully assign subjects to different treatment groups, so that those given each treatment are similar in ways that are important to the experiment.
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Sampling error
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the difference between a sample and the true population result, such an error results from chance sample fluctuations.
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Non-sampling error
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sample data incorrectly collected, recorded, or analyzed (such as by selecting a biased sample, using a defective instrument, or copying the data incorrectly.
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