What are the considerations for choosing a sample size?
When choosing a sample size, we must consider the following issues:
- What population parameters we want to estimate.
- Cost of sampling (importance of information)
- How much is already known.
- Spread (variability) of the population.
- Practicality: how hard is it to collect data.
- How precise we want the final estimates to be.
What is the advantage of choosing a larger sample size to represent a population?
Larger sample sizes provide more accurate mean values, identify outliers that could skew the data in a smaller sample and provide a smaller margin of error.
How to select a sample from a large dataset?
The simplest thing to do is taking a random sub-sample with uniform distribution and check if it’s significant or not. If it’s reasonably significant, we’ll keep it. If it’s not, we’ll take another sample and repeat the procedure until we get a good significance level.
Is it safe to ignore the sample size?
It is safe to ignore the fact that you are sampling from a finite population, since the population size N = 200, 000 is huge, compared to the sample size n. Essentially we assume that draws are independent. Each call can be classified into k = 7 categories.
Why are researchers interested in the size of the sample?
Once the survey has begun, the marginal costs associated with gathering more information, from more people, are proportional to the size of the sample. Researchers are not interested in the sample itself, but in the understanding that they can potentially infer from the sample and then apply across the entire population.
When is the population small enough for sampling?
When the population is small enough, researchers have the resources to reach out to all of them. This would be the best case scenario, making sure that everybody who matters to the survey is represented accurately.