I am currently going through the Survey of Data Science module. I am on page 3 “The data science process: Determine the Necessary Data”. I am hung up on the definition of Margin of Error. The text explains:
- Margin of error - The furthest we expect the true value to be from what we measure in our survey.
Is margin of error just a guess?
When playing with the calculator on the same page I was surprised to find that increasing the margin of error actually decreases the number of samples required to be an accurate sample of the population. Does increasing the margin of error always result in fewer samples to be tested?
The values of the calculator are:
Margin of Error: 5% Confidence Level: 90% Population Size: 50000 Likely Sample Proportion: 50% Sample Size: 269
The second calculation that had me scratching my head is:
Margin of Error: 15% Confidence Level: 90% Population Size: 50000 Likely Sample Proportion: 50% Sample Size: 30
Notice that all that changed was the margin of error increasing, that resulted in a lower sample number.
I understand that the confidence level modifies the margin of error assumption. Is this just a guess as well?
Is the calculator just giving me the minimum number of samples required to meet that confidence level, assuming the margin of error to be true?