To save time and money, you would probably survey a smaller group of Canadians. However, your finding may be different from the actual value if you had surveyed the whole population. That is, it would be an estimate. Each time you repeat the survey, you would likely get slightly different results.
Commonly, when researchers present this type of estimate, they will put a confidence interval CI around it. The CI is a range of values, above and below a finding, in which the actual value is likely to fall. The confidence interval represents the accuracy or precision of an estimate. We often see CIs in newspapers when the results of polls are released. An example from the Globe and Mail newspaper regarding the mayoral race in Toronto read, "52 per cent [of survey respondents] said they would have voted for Mr.
So when you write up your results, point out that the confidence intervals are wide, indicating that the sample size was too small better for you to admit it than to have a referee force you to make the statement. Then point out that any conclusions that you might try to draw from the data need to be replicated with a larger sample size.
Need more information? I have a page with general help resources. For example, if 5 percent of voters are undecided, but the margin of error of your survey is plus or minus 3. In one sample of voters, you might have 2 percent say they are undecided, and in the next sample, 8 percent are undecided. This is four times more undecided voters, but both values are still within the margin of error of the initial survey sample.
On the other hand, narrow confidence intervals in relation to the point estimate tell you that the estimated value is relatively stable; that repeated polls would give approximately the same results. Confidence intervals are calculated based on the standard error of a measurement. For sample surveys, such as the presidential telephone poll, the standard error is a calculation which shows how well the poll sample point estimate can be used to approximate the true value population parameter , i.
Generally, the larger the number of measurements made people surveyed , the smaller the standard error and narrower the resulting confidence intervals. Once the standard error is calculated, the confidence interval is determined by multiplying the standard error by a constant that reflects the level of significance desired, based on the normal distribution.
For example, the odds ratio of 0. As the confidence level increases, the confidence interval widens. There is logical correspondence between the confidence interval and the P value see Section If the P value is exactly 0. Together, the point estimate and confidence interval provide information to assess the clinical usefulness of the intervention.
Confidence intervals with different levels of confidence can demonstrate that there is differential evidence for different degrees of benefit or harm.
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