Why is it important that a sample be random and representative when conducting hypothesis testing? Representative Sample vs. Random Sample: An Overview Economists and researchers seek to reduce sampling bias to near negligible levels when employing statistical analysis. Three basic characteristics in a sample reduce the chances of sampling bias and allow economists to make more confident inferences about a general population from the results obtained from the sample analysis or study: * Such samples must be representative of the chosen population studied. They must be selected at random, meaning that each member of the larger population has an equal chance of being chosen. * They must be big enough so as not to bias the results. The optimal size of the sample group depends on the precise degree of confidence required for making an inference. Representative sampling and random sampling are two techniques used to help ensure data is free of bias. These sampling techniques are not mutually exclusive and, in fact, they are often used in tandem to reduce the degree of sampling error in an analysis and allow for greater confidence in making statistical inferences from the sample in regard to the larger group. Representative Sample A representative sample is a group or set chosen from a larger statistical population or group of factors or instances that adequately replicates the larger group according to whatever characteristic or quality is under study. A typical sample mirrors important variables and traits of the large society being studied. Some examples include sex, age, education level, socioeconomic status (SES), or marital status. A larger sample size reduced sampling error and increases the likelihood that the sample accurately reflects the target population. Random Sample A random sample is a group or set chosen from a larger population or group of factors of instances in a random manner that allows for each member of the larger group to have an equal chance of being chosen. A random sample is meant to be an unbiased representation of the larger population. It is considered a fair way to select a sample from a larger population since every member of the population has an equal chance of getting selected. Special Considerations: People collecting samples need to ensure that bias is minimized. Representative sampling is one of the key methods of achieving this because such samples replicate as closely as possible elements of the larger population under study. This alone, however, is not enough to make the sampling bias negligible. Combining the random sampling technique with the representative sampling method reduces bias further because no specific member of the representative population has a greater chance of selection into the sample than any other. Summarize this article in 250 words.
Summary of the Article- This article state about Sample and what things are important to consider when we conduct a hypothesis testing for our research. A sample is very important in research because it is a set of individuals, units or objects which researcher collect from a population. Since, Researcher can not collect data and test thier hypothesis from whole population so they take sample by defined procedure to inference thier results and then test thier hypothesis.The two techniques that help researcher to get reliable and error free results are random and representative sampling techniques. Randome sampling is a technique in which sample is collected from population randomly. In this technique, the sample is more true, free from bias. This is because randome sampling make sure each person or object have fair and equal chance to be chosen. Representative technique, as the name suggests, is a technique that accurately shows or represents the characteristics of a large population. The characteristics like age, gender, marital status, education level etc. for example- if we study the behavior of poor results of students in Maths and scienece then we collect sample. The class have 40 students where 20 are male and 20 are female students so we collect sample of 10 students where 5 represent male and 5 represent female students. The purpose of every study is to diminish the probability of containing biases and mistakes. Using both techniques will help researcher to achieve results with less bias and sampling error so that hypothesis testing can give real and true result.
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