"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."
This topic covers different sampling methods like random, stratified, cluster, and snowball sampling.
Population and Sample: This topic helps learners understand the difference between population and sample, and how researchers go about selecting samples for their studies.
Sampling Bias: This covers the various types of sampling bias that can affect a study, and ways to minimize such bias.
Probability Sampling: Outlines the different types of probability sampling methods and how they are used in research studies.
Non-probability Sampling: This covers the different types of non-probability sampling methods and when they are appropriate to use.
Sampling Techniques: This topic provides a detailed understanding of the different sampling techniques that researchers can use, such as random sampling and stratified sampling.
Sampling Frame: It explains the methods used by researchers to create a list of eligible people for a study known as a sampling frame.
Sampling Distribution: This topic covers the distribution of the statistical measures of a sample and how this may differ from the population.
Sample Size: Provides information on calculating the ideal sample size for a research study and determining the necessary factors to determine the required number of participants.
Sampling Error: This topic delves into the concept of sampling error, factors that can affect it, and ways to minimize it.
Outlier Detection: Discusses the methods researchers use to identify outliers and ways to handle them.
Stratified Sampling: This involves selecting participants based on specific characteristics or attributes to ensure a diverse representation in the sample.
Cluster Sampling: This occurs when participants are selected based on groups or clusters rather than individually.
Multistage Sampling: Describes a process of sampling that involves multiple stages of selection from different groups.
Convenience Sampling: This method of sampling involves selecting participants based on convenience rather than random selection.
Quota Sampling: This topic covers a method of sampling that involves selecting participants based on specific characteristics that the researcher needs for their study.
Random Sampling: A sampling method in which every member of the population has an equal chance of being selected.
Stratified Sampling: A sampling method in which the population is divided into strata (subgroups) based on some characteristic, then a random sample is selected from each stratum, and the samples are combined to form the final sample.
Cluster Sampling: A sampling method in which the population is divided into clusters (grouped based on geographic, institutional or other similar criteria), and a random sample of clusters is selected. Then, all members of the selected clusters are surveyed.
Convenience Sampling: A non-probability sampling method in which the researcher selects the easiest or most convenient individuals to participate in the study.
Quota Sampling: A non-probability sampling method in which a target number of participants is set for each of several subgroups based on demographic characteristics, and then the researcher selects individuals to meet those quotas.
Snowball Sampling: A non-probability sampling method in which the researcher selects initial participants, and then those participants help recruit additional participants for the study.
Multi-stage Sampling: This is a combination of sampling methods, and it involves taking a sample from several stages or levels.
Purposive Sampling: A non-probability sampling method in which the researcher deliberately selects individuals who have certain traits or characteristics.
Systematic Sampling: A sampling method in which participants of the population are chosen at fixed intervals, from a random starting point.
Voluntary Sampling: A non-probability sampling method in which the researcher posts an advertisement, and the research participants volunteer to participate in the study.
Double Sampling: This sampling method involves two stages of sampling. The first stage involves selecting a smaller sample from the larger population using probabilistic techniques, and the second phase involves sampling the selected sample again using a different type of probabilistic technique.
Sequential Sampling: A method used when it is not practical to select all the units in one go, whereby the sampling is carried out in a sequence of smaller units from the population.
Time Sampling: This method involves selecting observations at specific time intervals to analyze behavior over time.
Panel Sampling: A method in which the researcher selects the same group of individuals repeatedly over time to study the changes in their opinions, attitudes, or behaviors.
Disproportional Sampling: A sampling method in which the number of subjects selected from each stratum is not proportional to the size of that stratum in the population.
Haphazard Sampling: This is where the researcher selects random individuals without any knowledge of the population or any criteria.
Covert Sampling: In this form of sampling, the researcher is not transparent about what is been studied or observed, thus not informing the participants.
Systematic Sampling with Probability Proportional to Size: This is a variant of systematic sampling method where the units are selected with probability proportional to size i.e., the size of the units in the population.
Single Stage Sampling: This is a single-stage procedure where the sample is drawn from the first step of the process.
Indirect Sampling: A method where the sample is connected to the population only through another sample.
"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."