Sampling and sampling distributions

• random sampling - Method of choosing representatives from the population wherein every sample has an equal chance of being selected. Accurate data can be collected using this.
• set of all possible values. - Population
• consists of one or more data drawn from populations. - sample
• two types of sampling: probability sampling and non-probability sampling
• type of sampling that involves random selection and principle or randomization of chance. - probability sampling
• does not involve random selection of data. - non-probability sampling
• most basic random sampling. - simple random sampling
• each element has an equal probability of being selected. - simple random sampling
• examples of how to get the sample for this is: fishbowl , lottery method , and table of random numbers
• A list of the elements of the population is used as a sampling frame and the elements to be included in the desired sample are selected by skipping through the list at a regular interval. - systematic sampling
• population is first divided into strata/groups and then samples are randomly selected separately from each stratum/group. - stratified sampling.
• Population is divided into clusters then a cluster will be chosen to be the sample. - cluster sampling
• all individuals in the needed group/s from the population would be needed to answer/ participate. - cluster sampling