Discussion Topic

What is the best way of choosing a sample to
statistically represent a population? Why? What
is a biased sample? How can biased sampling
affect the statistical study of a population?

Search the Internet and give two or more real-
world examples of biased sampling leading to
unexpected or unfavorable outcomes. In each
case, how could the sample have been improved
to make a more accurate inference about the
population?

Sampling methods are classified as either probability or nonprobability.

Step-by-step explanation:

Random sampling is the purest form of probability sampling. Each member of the population has an equal and known chance of being selected. However, when the study population is large, it is often difficult to identify every member of the population, so the pool of available subjects might become bias.

Systematic sampling is often used instead of random sampling. It is also called an Nth name selection technique.

Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error.

Non-probablistic sampling techniques are: Convenience sampling is used in exploratory research where the researcher is interested in getting an inexpensive approximation of the truth.

Judgment sampling is a common nonprobability method. The researcher selects the sample based on judgment. This is an extension of convenience sampling.

Quota sampling is the nonprobability equivalent of stratified sampling. Like stratified sampling, the researcher first identifies the stratums and their proportions as they are represented in the population. Then convenience or judgment sampling is used to select the required number of subjects from each stratum. This differs from stratified sampling, where the stratums are filled by random sampling.

Snowball sampling is a special nonprobability method used when the desired sample characteristic is rare.

Biased sample is defined as a predisposition to one particular outcome over another from a study population. It leads to unrepresentative outcomes. In other words, bias causes researchers' estimates to be predisposed to the left or to the right of the true mark.

Biaed sampling lead to a systematic over- or under-estimation of the corresponding parameter in the population, that is, sample size becomes over represented or under-represented. Wen samples are bias, conclusion becomes inaccurate.

Life Examples of bias are: a survey of high school students to measure teenage use of illegal drugs will be a biased sample if it does not include home-schooled students. For this, the sample will become adequately represented if it includes home-schooled students.

Another example The hospital wants to draw a random sample from our master patient index for the purpose of a survey. Email is the desired communication method because of cost. From experience in information technology, the population containing actual email addresses may be skewed towards younger people. This could be bias and not provide a true representation of the patient population.

To address this, it is true that a higher percentage of younger patients have email. If we send a survey only to those who have an email, the survey will be biased, thus the sample size should be increased for the studies to reflect a true representation of the population.

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step-by-step explanation: