1. Incidence, Prevalence and 95% Confidence Intervals Tom Walker and Matt Newman

2. Prevelance Prevalence – proportion/percentage of current sufferers. (Point) prevalence - in winter, 4.4% of population has common cold at any given time. Period prevalence – 20% of UK adults suffer from the flu each year (12-monthly prevalence). Lifetime prevalence – Over 33% of people born today will be diagnosed with cancer during their lifetime.

3. Incidence Incidence is rate at which new cases appear. Formally: number of new cases per person per year. Incidence = E.g. Coventry has population 32 000. 500 people develop osteoporosis over 2 years. Incidence = 0.0078 (7.8 cases per 1000 people per year). People observed x years observed New cases observed

4. Incidence Calculation  Warwick university has a population of 20,000 students. Every 4 years they produce 600 of the Uks finest doctors. What is the incidence of doctors coming out of Warwick University.

5. Incidence Incidence = 600 20000 x 4 years Incidence = 7.5 per 1000 people

6. 95% Confidence Interval  Due to sampling variation observed and true values are going to be different.  We use a calculation to show how mathematically confident we are, that the true value lies within the an upper and lower interval.  If we keep sampling the same population, 95% of the confident intervals we create will contain the true value.

7. Building 95% Confidence Intervals II For estimated proportion p the 95% CI is: [p - 1.96 x SE, p + 1.96 x SE] ^ ^ ^ LEARN!

8. Example: we test 100 people for blood type O. 40 people are found to have it.

9. Building 95% Confidence Intervals II For estimated proportion p the 95% CI is: [p - 1.96 x SE, p + 1.96 x SE] So in blood type example, 95% CI is [0.4 – 1.96 x 0.049, 0.4 + 1.96 x 0.049] = [0.304, 0.496] ^ ^ ^

10. Wtf is with the standard error?  You don’t have to learn the standard error calculation!  It’s a mathamatical calculation to discern how spread out the sample is from the mean.  The larger the standard error, the further from the true value an observed value can be. Therefore a bigger standard error means wider interval.

11. Null Hypothesis  The null hypothesis is the hypothesis of no effect. I.e, our results show no relationship between cause and effect.  If the observed value falls outside the confidence interval, we can reject the null hypothesis  And therefore the result is statistically significant.  If the observed value falls inside the confidence interval, we can’t reject the null hypothesis, and the result is statistically insignificant.