Chi Square Distribution Table: in HTML Word document text file
Confidence Interval: The plus-or-minus percentage figure usually reported opinion poll results. For example, if you use a confidence interval of 5% and 50%% percent of your sample picks an answer you can be "sure" that if you had asked the question of the entire relevant population between 45% (50-5) and 55% (50+5) would have picked that answer.
Confidence Level : 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. Most researchers use the 95% confidence level.
When you put the confidence level and the confidence interval used above together, you can say that you are 95% sure that the true percentage of the population is between 45% and 55%.
Sample Size: The number of people polled in your survey. The larger your sample, the more sure you can be that their answers truly reflect the population.
Population Size: How many people in the total group from which your sample was taken.
Percentage: Your accuracy also depends on the percentage of your sample that picks a particular answer. If 99% of your sample said "Yes" and 1% said "No" the chances of error are remote, irrespective of sample size. However, if the percentages are 51% and 49% the chances of error are much greater. It is easier to be sure of extreme answers than of middle-of-the-road ones.
When determining the sample size needed for a given level of accuracy you must use the worst case percentage (50%). To determine the confidence interval for a specific answer your sample has given, you can use the percentage picking that answer and get a smaller interval.
Explanation of Statistical Significance