Contents
What assumptions must you make for the confidence interval to be valid?
Assumptions and Conditions
- Randomization Condition: The data must be sampled randomly.
- Independence Assumption: The sample values must be independent of each other.
- 10% Condition: When the sample is drawn without replacement (usually the case), the sample size, n, should be no more than 10% of the population.
Does confidence interval include bias?
It is important to note that 95% confidence intervals only address random error, and do not take into account known or unknown biases or confounding, which invariably occur in epidemiologic studies.
What can we assume if confidence intervals are overlapping?
If those intervals overlap, they conclude that the difference between groups is not statistically significant. If there is no overlap, the difference is significant.
What causes the size of a confidence interval?
There are three factors that determine the size of the confidence interval for a given confidence level. These are: sample size, percentage and population size. Sample Size. The larger your sample, the more sure you can be that their answers truly reflect the population.
How does sample size affect your confidence level?
Sample Size The larger your sample, the more sure you can be that their answers truly reflect the population. This indicates that for a given confidence level, the larger your sample size, the smaller your confidence interval. However, the relationship is not linear (i.e., doubling the sample size does not halve the confidence interval).
What does the confidence level of a question mean?
The confidence level tells you how sure you can be. It is expressed as a percentage and represents how often the true percentage of the population who would pick an answer lies within the confidence interval. The 95% confidence level means you can be 95% certain; the 99% confidence level means you can be 99% certain.
What does it mean to have a 95% confidence level?
It is expressed as a percentage and represents how often the true percentage of the population who would pick an answer that lies within the confidence interval. The 95% confidence level means you can be 95% certain; the 99% confidence level means you can be 99% certain. Most researchers work for a 95% confidence level.