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Lecture 4: Sampling
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Terms in this set (26)
Census
Surveying every item in the population
Sampling design
The method we use to choose the sample from the population
Inference
The process of drawing conclusions regarding the population from the sample statistics
Simple Random Samples
A sample in which every individual in the population has an equal chance of being selected
Chance Error/Variation
Changes from measurement to measurement or sample to sample variations
Bias
Underestimates or overestimates a true value for instance the mean income in a study. Bias effects all the measurement in the same manner. It pushes all the values up or down
Sampling Bias
A bias that occurs if the sample collected is not representative of the population.
Self Interest Bias
A bias that can be identified when the researcher collects data in a particular way that will benefit their cause. It will serve to be self advantageous at the costs of others.
Voluntary Response Bias
A bias that can be identified when a researcher collects data in a manner that is convenient for them.
Non-response Bias
A bias that is deciphered when the sample collected contains unanswered responses.
Leading Question Bias
A bias that comes forth if the survey questions lead individuals to answer them as they would not wish to.
Social Acceptability Bias
A bias that is observed when a sample does not answer a question honestly due to the fear of social disapproval.
Selection Bias
A bias that occurs due to the inaccurate procedure used to collect data
Under coverage bias
Some groups in the population are not included in the sampling process.
Sampling Errors
The error due to difference between sample measure and population measure. This error can be attributed to chance variation
Non Sampling Error
The error caused due to the inaccurate data collection, recording, and analysis
Sampling bias
A systematic error that is incorporated due to the wrong sampling method
Probability Sampling
The probability of selecting one individual is the same as the probability of selecting any other individual in a population. (Includes: Simple random sample, stratified random sample, cluster random sample, systematic random sample, multistage sampling)
Non Probability Sampling
The probability of selecting one individual is not the same as the probability of selecting any other individual in the population. (Includes: Convenience sampling, voluntary sampling, quota sampling)
Simple random sampling
Every entity in the population has an equal chance of being selected in the sample (without replacement)
Systematic Random Sampling
The population is numbered and then every "kth" number is chosen to create a systematic sample.
Voluntary Response Sampling
The samples from the population are chosen as per the voluntary response of the participants
Convenience Sampling
The samples from the population are chosen as per the convenience of the researcher
Stratified Random Sampling
The population is divided into homogeneous groups/stratas using a criterion. From each strata a random sample is taken
Cluster Random Sampling
Population is divided into homogenous clusters and within the cluster there are heterogeneous elements. A few clusters chosen using random sampling and all elements of those clusters are surveyed.
Quota Sampling
Population is divided into certain categories and some members from each quota are chosen
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