What would you like to do?

• Test significance of sample proportions (Z-Test)
      More information on Z-Tests
  
• Test significance of sample means (T-Test)
      More information on T-Tests
  
• Sample Precision Calculator
      More information on Sample Precision
  
• Frequently Asked Questions (FAQ's)
• What is "Statistical Significance"?
• A sample of observations, such as responses to a questionnaire, will generally not reflect exactly the same population from which it is drawn. As long as there is variation among individuals, sample results will be affected by the particular mix of individuals chosen. "Sampling error" (or conversely, "sample precision") refers to the amount of variation likely to exists between a sample result and the actual population.
• "Confidence level" qualifies a statistical statement by expressing the probability that the observed result cannot be explained by sampling error alone. To say that the observed result is significant at the 95% confidence level is to say that there is a 95% chance that the difference is real and not just a quirk of the sampling.
• "Sampling error" and "confidence levels" work hand-in-hand. A larger difference may be significant at a lower confidence level. For example, we might be able to state that we are 95% confident that a sample result falls within a certain range of the true population level. However, we can be 90% confident that our sample result falls within some broader range of the population level.
• When comparing two observed survey results, the underlying principles are the same. The key question of whether a difference is "significant" can be restated as "is the difference enough to allow for normal sampling error?". This is the question that z-tests and t-tests can provide helpful data on.
• How large a sample is enough?
• Is a 1- or 2-tailed test appropriate?