There will be general elections in the country of Samplonia. The National Elderly Party (NEP) seems to do well in the campaign. An opinion poll is carried out to estimate the percentage of voters this party will attract. To determine how precise the estimator is, sample selection is repeated a large number of times. The percentage of voters is computed for each sample. The distribution of all these estimates is shown in a histogram.
The average of all estimates is computed. The estimators is unbiased if this average is (approximately) equal to the true population percentage (25.4%).
p>To carry out a simulation, you first set the sample size. You do that by clicking on the green square adjacent to Sample size. There are three possible sample sizes: 200, 400 or 800.
You can choose to generate non-response in the survey. You do that be clicking on the green square below Non-response. The probability of non-response increases with age in this demonstration. For young people, this probability is equal to 80%, fior middle-aged people it is 50%, and for elderly, the probability of non-response is 20%.
You start the simulation by cliking on Start.
If there is no non-response, the estimates will be neatly concentrated around the true percentage of voters in the population (25.4%). If there is non-response, the estimates will be significantly too low.
Why are to estimates too low? The reason is the elderly are unde-represented in the samples, because non-response is highest among them. It are the elderly who vote for NEP. So, there will be too few NEP-voters in the samples.
Note that non-response causes the variation of the estimates to increase. This is alo a typical non-response effect. Non-response reduces the sample size, and therefore increases the variance of estimators, leading to larger margins of error.