Three ideas of Sampling 1 Examine a Part of the Whole o Sample Survey small group of people in the hope of learning something about the entire population survey designed to ask questions of a o When a sample is biased the summary characteristics of a sample differ from the corresponding characteristics of the population it is trying to represent 2 Randomize o Randomization a defense against bias in the sample selection process in which each individual is given a fair random chance of selection o The variability from sample to sample is often referred to as sampling error even though no error has occurred 3 The Sample Size is What Matters o The size of the sample determines what we can conclude from the data regardless of the size of the population o Many people think that we need a large percentage or fraction of the population but in fact all that matters is the size of the sample A Census Does It Make Sense Census An attempt to collect data on the entire population of interest It can be difficult to complete a census It could be bad information because the population may change It can be cumbersome many errors Populations and Parameters Parameter Sample Statistic Population Parameter A numerically valued attribute of a A numerically valued attribute of a model for a population We rarely expect to know the value of a parameter but we do hope to estimate it from sampled data model for a population particularly one that corresponds to and thus estimates a population parameter Representative Sample A sample from which the statistics computed accurately reflect the corresponding population parameters Simple Random Sample SRS a value calculated for sampled data SRS A sample in which each set of n elements in the population has an equal chance of selection Sampling frame will be drawn a list of individuals from which the sample Sampling Variability or sampling error isn t a problem Other Sampling Designs Stratified Sampling o Strata o Stratified Sampling subsets of a population that are internally homogeneous but may differ one from another population is divided into several homogeneous subpopulations or strata and random samples are drawn from each stratum Cluster and Multistage Sampling A sampling design in which the o Cluster o Cluster Sampling A representative subset of a population chosen for reasons of convenience cost or practicality A sampling design in which groups or clusters representative of the population are chosen at random and a census is taken of each o What s the difference between cluster and stratified sampling Strata are homogeneous but differ from one another Clusters are more or less alike each heterogeneous and resembling the overall population o Sampling schemes that combine several methods are called multistage samples Systematic Samples o Systematic Sampling individuals systematically from a sampling frame A sample drawn by selecting Defining the population The sampling frame limits what your survey can find out Target sample are the individuals whom you intend to measure responses You re not likely to get responses from all of them The Valid Survey Use the right sampling frame Know what you want to know Ask specific rather than general questions Watch for biases Be Careful with question phrasing Be careful with answer phrasing o Measurement Errors or unintentional sampling frame and a draft form of the survey instrument is administered o Pilot Test a small sample that is drawn from the inaccurate responses intentional Convenience sampling are convenient when you include the individuals who Undercoverage sampled at all or has a smaller representation in the sample than it has in the population in which some portion of the population is not
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