It is very important to understand the concept of sampling error as it indicates the inaccuracy of the sample survey. Sampling Error = Z * √(p * (1 – p) / n) * (1 – √(n /N)) Relevance and Use of Sampling Error Formula Step 5: Final, the formula for sampling error can be derived based on the confidence level of the estimation (step 1), sample size (step 2), population size (step 3) and proportion of the population with a set response (step 4) as shown below. So, (1 – p) denotes the percentage of the people with the alternate response. It is expressed in percentage and denoted by “p”. Step 4: Next, determine the proportion of the people surveyed who are likely to respond either in a positive way or say “yes” as an answer to the survey question. Step 3: Next, determine the size of the entire population that is denoted by “N”. its sample characteristics will mostly be similar to that of the entire population. It is the proportion of the population that is expected to represent the entire population, i.e. Step 2: Next, determine the sample size for the estimation. For instance, the z-score for a 95% confidence level is 1.96. Based on the selected confidence level, the z-score can be determined that is denoted by “Z”. Step 1: Firstly, decide on the confidence level to be used for the estimation. The formula for Sampling Error analysis can be computed by using the following steps: So, samples are a better representative of the smaller data population. Therefore, it can be seen that the sampling error decreases with a decrease in population size. Therefore, the sampling error for the sample at a 99% confidence level is 2.64%.
Therefore, the sampling error for the sample at 95% confidence level is 2.01%. Sampling Error is calculated using the formula given below
Calculate the sampling error for a 95% confidence level and a 99% confidence level. Now, again let us take the example of the above example and keep everything the same except the population size, which is to be assumed to be significantly lower in this case, say 2,000. Therefore, it can be observed that the sampling error of any sample increases with the increase in confidence level.
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