Statistical Gallery

Bias in Survey Sampling In survey sampling, bias refers to the tendency of a sample statistic to systematically over- or under-estimate a population parameter . Bias Due to Unrepresentative Samples A good sample is representative . This means that each sample point represents the attributes of a known number of population elements. Bias often occurs when the survey sample does not accurately represent the population. The bias that results from an unrepresentative sample is called selection bias . Some common examples of selection bias are described below. Undercoverage . Undercoverage occurs when some members of the population are inadequately represented in the sample. A classic example of undercoverage is the Literary Digest voter survey, which predicted that Alfred Landon would beat Franklin Roosevelt in the 1936 presidential election. The survey sample suffered from undercoverage of low-income voters, who tended to be Democrats.

Types of Sampling :    There are divided by Probability            Simple Random             Systematic Random                Stratified Random                      Cluster sampling                         Multistage Sampling Non-Probability                Convenience Sampling                      Purposive Sampling                                   Probability vs. Non-Probability Samples As a group, sampling methods fall into one of two categories. Probability samples . With probability sampling methods, each population element has a known (non-zero) chance of being chosen for the sample. Non-probability samples . With non-probability sampling methods, we do not know the probability that each population element will be chosen, and/or we cannot be sure that each population element has a non-zero chance of being chosen. Non-probability sampling methods offer two potential advantages - convenience and cost. The main disadvantag

Population                A   population   is a group of individuals(or)aggregate of objects under study.It is also known as universe. The population is divided by (i)finite population  (ii)infinite population, (iii) hypothetical population,  subject to a statistical study . A population includes each element from the set of observations that can be made. (i) Finite population : A population is called finite if it is possible to count its individuals. It may also be called a countable population. The number of vehicles crossing a bridge every day, (ii) Infinite population : Sometimes it is not possible to count the units contained in the population. Such a population is called infinite or uncountable. ex, The number of germs in the body of a patient of malaria is perhaps something which is uncountable   (iii) Hypothetical population : Statistical population which has no real existence but is imagined to be generated by repetitions of events of a certain typ