"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."
Techniques used to select a representative sample of participants from a larger population.
Population and Sample: Defining the population of interest and the process of selecting a subset of individuals to represent it.
Sampling Techniques: Different methods used to select a sample, like simple random sampling, stratified random sampling, cluster sampling, and systematic sampling.
Sampling Error and Bias: Understanding the difference between sampling error and bias, and the impact they can have on the validity and generalizability of research findings.
Probability Sampling: Learning about probability sampling methods, which involve random selection and allow for the calculation of statistical estimates of population parameters.
Non-probability Sampling: Understanding non-probability sampling methods, which do not involve random selection and do not allow for statistical estimates of population parameters.
Sampling Size: Determining the appropriate sample size for a study based on factors like the desired level of precision and the variability of the population.
Sampling Frame: Identifying and describing the population from which the sample is drawn, as well as any potential sources of bias in the sampling frame.
Sampling Distribution: Understanding the concept of a sampling distribution and the role it plays in statistical inference.
Sampling Methods in Qualitative Research: Understanding sampling methods in qualitative research, such as purposive sampling, snowball sampling, and theoretical sampling.
Sampling Methods in Survey Research: Learning about sampling methods in survey research, such as stratified random sampling, quota sampling, and convenience sampling.
Simple random sampling: It is a method in which every individual in a population has an equal chance of being selected.
Stratified sampling: This method is used when the population is divided into subgroups based on important variables, such as age or gender, and then individuals are randomly selected from each group.
Cluster sampling: It involves dividing a population into groups or clusters based on their geographic location or other characteristics, and then sampling a few of these clusters at random.
Systematic sampling: This method is used when the population is already in a specific order, and individuals are selected at regular intervals from that ordered list.
Convenience sampling: In this method, individuals are selected based on their accessibility and willingness to participate.
Snowball sampling: In this method, the initial participants are selected through convenience sampling, and then they are asked to refer other individuals who meet the criteria for the research project.
Purposive sampling: Also known as judgmental sampling, this method involves selecting individuals who are deemed to be representative of the population based on the expert judgment of the researcher.
Quota sampling: This method involves selecting a certain number of individuals from each subgroup based on pre-determined quotas.
Time-space sampling: This method involves randomly selecting individuals at different times, and in different locations or settings, to obtain a representative sample of the population.
Multi-stage sampling: This method is a combination of two or more sampling techniques to obtain a representative sample of the population. For example, Stratified random sampling followed by systematic sampling could be used in a multi-stage sampling.
Panel sampling: It involves selecting a group of individuals over time who are representative of the population being studied.
Response rate sampling: This method involves using only the respondents who actually provide the requested data for analysis.
"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."