What is a sample in statistics?
Q: What is a sample in statistics?
A: In statistics, a sample is part of a population that has been carefully chosen to represent the whole population fairly and without bias.
Q: Why are samples needed?
A: Samples are needed because populations may be so large that counting all the individuals may not be possible or practical. Therefore, solving a problem in statistics usually starts with sampling.
Q: How is a sample represented?
A: When treated as a data set, a sample is often represented by capital letters such as X and Y, with its elements being represented in lowercase (e.g., x3), and the sample size being represented by the letter n.
Q: What should samples be?
A: As a general rule, samples need to be random which means the chance or probability of selecting one individual is the same as the chance of selecting any other individual. In practice, random samples are always taken by means of a well-defined procedure.
Q: Can bias remain in samples?
A: Even when using well-defined procedures for sampling some bias may remain in the sample due to factors like who answers phone calls or who walks on certain streets when collecting opinions for an election poll prediction. In cases like this it can be difficult to obtain completely neutral samples but statisticians can measure how much bias remains present.
Q: Are there different kinds of samples?
A: Yes, there are different kinds of samples including complete samples which include all elements that have given properties and unbiased/representative samples which involve selecting elements from complete samples without depending on their properties. The way sampling is obtained along with its size will impact how data is viewed.
