In Bayesian Inference, we do not assume that the parameter (the value that we are calculating like Reliability) is fixed. There is the ubiquitous 90/90 or 95/95 confidence/reliability sample size table that is used for this purpose. Ideally, one would want to have some confidence that the widgets being produced is x% reliable, or in other words, it is x% probable that the widget would function as intended. In today’s post, I have attached a spreadsheet that calculates the reliability based on Bayesian Inference.
One of the most common questions a statistician is asked is “how many samples do I need – is a sample size of 30 appropriate?” The appropriate answer to such a question is always – “it depends!” Learn more here.I have written about sample size calculations many times before. The reliability of survey estimates are commonly measured by a group of indicators, including the standard error, relative standard error and confidence interval. are there differences among those working in the Northern part of Coconut Island than those in the Southern part). does the income of the training participants differ widely?), andĬharacteristics studied (e.g. do we gross up the survey results to the population using only one or multiple expansion factors?), the number of training participants to be interviewed),Įstimation method (e.g. how the training participants are selected for the survey),
Take a look at the example below.įactors influencing the sampling error include the:
This extent of variation between estimates obtained from a sample and the true value is known as the sampling error. Estimates based on the sample selected will generally differ from those of the entire population. While this can help us save resources, no gain comes without a price. In a sample survey, we infer results about the target population by studying a representative subset of the population. If you ask this question, you are concerned with the precision of the data collected.
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