Topic2DQ 1PSY520

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Topic 2 DQ 1 " The process of random sampling guarantees that the sample selected will be representative of the population. Why is this statement not true? What other factors and variables influence the outcomes of studies?" The reason the this statement is not true is because random sampling is an effective way to reduce bias and increase the probability of obtaining a sample that is representative of the population. However, it does not ensure that the results obtained are representative of the population as a whole. Additionally, there are a variety of other elements and variables that can affect the results of studies, such as (Kelly, 2023): Sampling Bias : Sampling bias, even when random sampling is employed, is a phenomenon in which a subset of the population is disproportionately included in a sample compared to another subset. This can be caused by a variety of factors, including non-response biases, in which a subgroup of the population is less likely to participate in surveys or studies, and selection bias, in which the sampling method is not representative of the population as a whole (The Economic Times). Sample Size: Sample size can also affect the results of studies. Larger sample sizes tend to improve the precision and representativity of the sample, while smaller sample sizes may still be representative provided they are selected using suitable techniques and are not biased. Measurement Error : Measurement Error can also affect the results of a study. Measurement inaccuracy can be caused by equipment failure, human mistakes, or discrepancies in the measurement methodology. Confounding Variables : Confounding variables refer to variables that are associated with both independent and dependent variables within a study. This makes it challenging to determine the relationship between independent and dependent variables. For instance, a study looking at the association between exercise and body weight loss may be affected by a variety of factors, including dietary habits, genetic makeup, or underlying medical conditions. Sampling Frame : A sampling frame is a compilation of individuals or groups from which to select a sample. An incomplete or inaccurate sampling frame can result in a biased sample selection process. For this reason, random sampling is not the only way to obtain representative samples. Researchers must take into account and address other variables and factors that may affect the results of the study in order to guarantee the accuracy and reliability of the results. When performing statistical analysis, economists and researchers aim to minimize sampling bias to a level that is close to zero. The risk of sampling bias lies in the fact that it can lead to a skewed sample of a population or non-human factors in which all individuals or instances
were not equally probable to have been selected. Random sampling and representative sampling are two statistical techniques employed by researchers to obtain statistical information about a collection of data without examining the entire population. When used together, these two techniques can help to guarantee that statisticians are validating their data on a subset of the population. The Economic Times. (n.d.). What is random sampling? . The Economic Times. Kelly, R. C. (2023, August 9). Representative sample vs. Random Sample: What's the difference? Investopedia. difference-between-representative-sample-and-random-sample.asp
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