"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."
Methods of sampling and its applications in probability.
Population and Sample: Population refers to the complete set of individuals or objects that are of interest for statistical analysis. A sample is a subset of the population. This topic introduces the concept of sampling.
Types of Sampling: Different types of sampling techniques, such as random sampling, stratified sampling, cluster sampling, and systematic sampling, are discussed, with their advantages and disadvantages.
Sampling Distribution: This topic explains the probability distribution of a statistic computed from multiple samples drawn from a population. It includes the Central Limit Theorem and its applications.
Sample Mean and Variance: The sample mean and variance are important parameters of a sample that can provide information about the population. This topic discusses their properties and estimation.
Sampling Error and Bias: Sampling error refers to the difference between the sample statistic and the population parameter, while bias refers to the systematic distortion of the estimate. This topic addresses methods to reduce sampling error and bias.
Confidence Interval: A confidence interval is a range of values that is likely to contain the true population parameter. This topic explains how to compute confidence intervals and how to interpret them.
Hypothesis Testing: Hypothesis testing is a statistical tool that is used to make decisions about population parameters based on sample data. This topic introduces the basic framework of hypothesis testing and its components.
Sampling Methods and Sample Size: This topic discusses the influence of sampling method and sample size on the accuracy and precision of sample estimates. It includes concepts such as sampling distribution and power analysis.
Non-Probability Sampling: This topic covers sampling techniques that do not give each individual in the population an equal chance of being selected. Such techniques include quota sampling, judgmental sampling, and snowball sampling.
Sampling in Quality Control: Sampling is a crucial component of quality control in manufacturing and other industries. This topic explains how to design sampling plans and how to interpret statistical process control charts.
Simple random sampling: A sampling method in which every member of the sample has an equal chance of being selected.
Stratified random sampling: A sampling method in which the population is divided into homogeneous groups, called strata, and a random sample is taken from each group.
Cluster sampling: A sampling method in which the population is divided into clusters, and a random sample of clusters is selected, with all members of the clusters being included in the sample.
Systematic sampling: A sampling method in which members of the population are selected at regular intervals, using a predetermined system.
Multi-stage sampling: A sampling method that involves a combination of different sampling methods, such as stratified random sampling and cluster sampling.
Probability proportional to size sampling: A sampling method in which the probability of selecting a member of the sample is proportional to its size relative to the total size of the population.
Adaptive sampling: A sampling method that adjusts the selection criteria based on the data collected so far.
Double sampling: A sampling method in which a preliminary sample is taken to estimate the characteristics of the population, and then a second sample is taken based on these estimates.
Sequential sampling: A sampling method that involves taking additional samples until a predetermined level of precision is achieved.
Systematic probability sampling: A sampling method in which the population is divided into subgroups, and a sample is selected from each subgroup using a random method.
"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."