"Sampling is the selection of a subset or a statistical sample of individuals from within a statistical population to estimate characteristics of the whole population."
Ways to select study participants and calculate the appropriate sample size.
Sampling Techniques: The various techniques used in selecting a sample for a study, such as simple random sampling, stratified sampling, cluster sampling, and systematic sampling.
Sample Size Calculation: The process of determining the appropriate sample size for a study based on statistical power, level of significance, and effect size.
Bias in Sampling: The types of bias that can occur in sampling, such as selection bias, measurement bias, and confounding bias, and how to minimize their impact.
Randomization: The process of randomly assigning participants to a treatment or control group in order to reduce bias and increase the internal validity of a study.
Statistical Inference: The process of drawing conclusions about a population from a sample, and the various methods for doing so, such as confidence intervals and hypothesis testing.
Non-Probability Sampling Techniques: Sampling techniques that do not involve random selection, such as convenience sampling, quota sampling, and snowball sampling.
Power Analysis: The process of determining the statistical power of a study, which is the likelihood of detecting a true effect if one exists.
Stratification: The process of dividing the population into subgroups based on certain criteria, such as age or gender, in order to increase the precision of estimates and reduce variability.
Sampling Frames: The list of all individuals in the population from which the sample will be selected, and how to ensure that the sampling frame is representative of the population.
Cluster Sampling: A sampling technique in which groups of individuals are randomly selected rather than individual subjects.
Sampling Error: The difference between the sample estimate and the true population value due to chance variation in the sample.
Precision and Accuracy: The concepts of precision (the degree of consistency or reproducibility of a measurement) and accuracy (the closeness of a measurement to the true or accepted value) and their importance in sampling and study design.
Recruitment and Retention: Strategies for recruiting and retaining participants in a study, and the impact of recruitment and retention on the generalizability and external validity of a study.
Sampling Methods in Qualitative Research: Sampling methods used in qualitative research, such as purposive sampling, snowball sampling, and theoretical sampling.
Sampling Variability: The variation in sample estimates that would be obtained if many different samples were drawn from the same population.
Simple random sampling: Selecting participants at random from the population without any bias. Each member of the population has an equal chance of being selected.
Stratified random sampling: Dividing the population into several strata or sub-groups, by using specific criteria such as age, gender, or income level, and then selecting participants at random from each stratum.
Cluster sampling: Dividing the population into clusters based on geographic location, community or institution. A sample is then taken from each cluster and the data is analysed.
Systematic sampling: Selecting every nth member of the population to participate in the study. This is a common method used in surveys.
Convenience sampling: Selecting participants who are easily accessible or available, while ignoring those who are not readily available.
Quota sampling: Selecting participants based on pre-determined criteria such as age or gender in order to match the population demographics.
Purposive sampling: Selecting participants who have specific characteristics or traits, which are relevant to the research question.
Snowball sampling: Selecting participants who know other potential participants, who will then be recruited as well, forming a chain reaction or 'snowball' effect.
Judgmental sampling: Selecting participants based on expert judgement or knowledge about the population being studied.
"Statisticians attempt to collect samples that are representative of the population."
"Sampling has lower costs and faster data collection compared to recording data from the entire population."
"It can provide insights in cases where it is infeasible to measure an entire population."
"Each observation measures one or more properties (such as weight, location, colour, or mass) of independent objects or individuals."
"In survey sampling, weights can be applied to the data to adjust for the sample design, particularly in stratified sampling."
"In business and medical research, sampling is widely used for gathering information about a population."
"Acceptance sampling is used to determine if a production lot of material meets the governing specifications."
"In statistics, quality assurance, and survey methodology, sampling is the selection of a subset or a statistical sample of individuals from within a statistical population."
"The selection of a subset or a statistical sample of individuals from within a statistical population to estimate characteristics of the whole population."
"Sampling in statistics, quality assurance, and survey methodology is the selection of a subset or a statistical sample of individuals."
"Statisticians attempt to collect samples that are representative of the population."
"Sampling has lower costs and faster data collection compared to recording data from the entire population."
"Weights can be applied to the data to adjust for the sample design, particularly in stratified sampling."
"Business and medical research widely use sampling for gathering information about a population."
"Acceptance sampling is used to determine if a production lot of material meets the governing specifications."
"Results from probability theory and statistical theory are employed to guide the practice."
"Sampling is the selection of a subset or a statistical sample of individuals from within a statistical population."
"Results from probability theory and statistical theory are employed to guide the practice."
"Weights can be applied to the data to adjust for the sample design, particularly in stratified sampling."