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These flashcards cover key concepts related to sampling bias, sampling techniques, data collection methods, and statistical analysis as discussed in the lecture.
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Sampling Bias
A distortion of statistical analysis results due to a sample that is not representative of the population being studied.
Homogeneous Sample
A sample that consists of similar characteristics among all participants, reducing sampling bias.
Probability Sampling
A sampling method where every member of the population has a known chance of being selected, ensuring a representative sample.
Non-Probability Sampling
A sampling method where the selection of participants is based on subjective judgment rather than random selection.
Inclusion Criteria
The characteristics that participants must have to be included in a study.
Exclusion Criteria
The characteristics that disqualify potential participants from being included in a study.
Power Analysis
A statistical calculation to determine the necessary sample size needed to detect an effect in a study.
Confidence Interval
A range of values that is likely to contain the population parameter with a specified level of confidence.
P-value
A statistical measure that indicates the probability of observing the data assuming the null hypothesis is true.
Effect Size
A measure of the strength of a phenomenon or the size of the difference between groups.
Test-Retest Reliability
The consistency of a measure when it is administered to the same participants on different occasions.
Inter-Rater Reliability
The degree of agreement among raters or observers measuring the same phenomenon.
Descriptive Statistics
Statistics that summarize the basic features of the data in a study.
Inferential Statistics
Methods that allow inferences and predictions to be made about a population based on a sample.
Quota Sampling
A non-probability sampling technique that involves choosing a specific number of participants from different strata.
Randomization
The process of assigning participants to different groups in a study by chance.