![]() If using simple random sampling, we are free from the classification of data.It reduces the selection bias from the population because we are not taking out data by any strategic method.Sampling error is lowest in this method out of all the methods. Simple random sampling reduces the chances of sampling error.The size is necessary to be large but in the large data, it is impossible to maintain the quality of the population.The size need to be small if one want to use sample random sampling. ![]() It is not an efficient approach because sampling represent the entire data and if there is any error in the samples then the result can be deviated differently, that should be arise.Major disadvantage is sample out of large population which is costly and large time consuming instead one can go for other sample methods in data that is highly credit.There can be a sample selection bias in simple sampling as some data is critical which need to be selected but that can be missed out in the large population so having a simple sampling is easy but not of much use in that case. ![]() reason of using stratified and cluster sampling in which data is categorized into multiple category and then the item is picked in each category. Using simple random sampling consider that data is same, but it is not true in all the cases i.e.Limitations of Using Simple Random Sampling Data Collection from different area is not filtered in a later stage after initial input collection.It uses the concept of fair and equitable basis that chances are almost equal for every item in the total population.When the cost of using data is not so high then it is cost effective method.Simple random sampling is used when the research department is totally unknown about the facts of the population.It is totally unbiased so people consider it as it is transparent and simple to use.It consider that the population data is not skewed and not dependent on selection of any particular sample item.When the data is too large then it is good to use simple random sampling because chances of getting selection of each sample is almost same.Simple random sampling assumes that the population has no anomaly i.e.It is as simple sampling that can be applied in general in our day to day life.It is used when there is a population data which is homogeneous.Unable to generalize data: convenience sampling does not reflect the total population, so it will be difficult to generalize results that will apply to everyone.If taken the random sample then they are categorized as:.Also, as participants are volunteers, those that agree to participate may be biased on specific topics. Selection bias: you may exclude demographic subsets related to choosing participants in a particular area.Positivity bias: if those collecting data are aware of the result you seek, they may want to please you by choosing participants whom they surmise will support your hypothesis.Difficult to replicate results: because the population will vary in most locations, it is challenging to replicate your results.Low external validity: because convenience sampling is a starting point, if you base your research on it-without replicating results or adding in a probability sampling method-your findings may lack credibility.And because you will subjectively be choosing each individual to ask if they wish to participate, there is the possibility of bias. ![]()
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