Many of the reserachers collect data through non probality sampling method and apply paramertic test like 't' test for inferences. They argue that if the sample size is large enough, some kind of normality will be there and they have constraints in randomisation .
Are the data collected from non probability sample compitable to parametric test , like 't' and 'F' test ?
Hi,
Data collected from non-probability samples may not be compatible with parametric tests like the t-test and F-test, as these tests typically assume that the data are normally distributed and that the samples are selected randomly from the population.
Non-probability sampling methods such as convenience sampling, purposive sampling, or quota sampling do not involve random selection, which can result in biased samples that do not accurately represent the population. In such cases, the assumptions of the parametric tests may not hold, and using these tests could lead to incorrect conclusions.
However, there are alternative nonparametric tests that do not rely on the assumptions of normality or random sampling, such as the Wilcoxon rank-sum test or the Kruskal-Wallis test, which may be more appropriate for analyzing data from non-probability samples. These tests are designed to compare groups based on ranks rather than means, and are less sensitive to non-normality and non-random sampling.
It is important to carefully consider the sampling method and characteristics of the data when choosing an appropriate statistical test.
The compatibility of data collected from non-probability sampling methods with parametric tests like t-test and F-test depends on several factors, such as the sample size, distribution of the data, and the type of non-probability sampling method used.
Non-probability sampling methods do not guarantee that each unit of the population has an equal chance of being included in the sample. Therefore, the sample may not be representative of the population, and the results may not be generalizable. In addition, non-probability samples may have a bias that could affect the results of parametric tests.
However, if the sample size is large enough, the central limit theorem suggests that the sampling distribution of the mean will be approximately normal, even if the population distribution is not normal. In such cases, parametric tests like t-test and F-test can be used to make inferences about the population mean and variance. However, if the sample size is small, or if the data are heavily skewed or have outliers, then non-parametric tests may be more appropriate.
In summary, the compatibility of data collected from non-probability sampling methods with parametric tests depends on several factors, and researchers should carefully consider the limitations and assumptions of both the sampling method and the statistical test used.
If a sample is biased, then increases in sample size will be of no use.
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