When using survey data, researchers cannot be 100% sure what the measure they are studying would look like if they could count every person/event in the entire community. But, researchers can use statistics to create a range in which they are relatively certain that true count would fall.
In reports using sample data, Forsyth Futures uses a 95% confidence interval as that range, which means that analysts are 95% sure that the true number for the whole community falls within that range. For example, when using data from the American Community Survey to estimate the number of Forsyth County residents living below the poverty threshold, researchers cannot know the exact number of residents in poverty, but they can be 95% certain that the actual number of residents in poverty fall within a specific range.
Total count data does not have uncertainty introduced by how a sample was chosen, but there is some variation that is introduced by random chance. For example, whether a low-birthweight infant is born in December of one year or January of the next year, is a matter of chance, but it could have a slight effect on the rate of low-birthweight births for that year.
Typically rarer events and community characteristics are more sensitive to these random fluctuations than more common events and community characteristics. For example, because the number of suicides in Forsyth County are much lower than the number of babies born at a low birth-weight, a fluctuation of one or two suicides a year due to random chance can have a bigger impact on the suicide rate than the random fluctuation of one or two low-birthweight births.
When using total count data, Forsyth Futures analysts calculate a 95% confidence interval for each statistic. This confidence interval for these statistics represents the estimated range of natural fluctuation around the statistic. For example, in 2014 Forsyth County had a low infant birthweight rate of 9.54% with a 95% confidence interval of 8.65%-10.44%. At face value, this appears to be a decrease from the 2013 rate of 10.39%, but since the 2013 rate falls within the confidence interval of the 2014 rate, analysts cannot be at least 95% sure that this difference is not due to random chance.