Statistical Analysis

For a generator to draw accurate conclusions about waste, the data must represent the waste. Representative data comes from a sample that can be expected to exhibit the average properties of the whole waste. Statistically analysis of data shows whether the samples taken are truly representative of the waste in total.

Once the analytical data is obtained, it is important to confirm whether the data is “representative” of the whole waste – whether it accurately predicts the characteristics of all the waste. The two concepts associated with the representative-ness of data are accuracy and precision.

Accuracy Accuracy is the measure of how closely the data from the sample is to the true average properties of the waste. Usually, accuracy increases with random selection of samples.
Precision Precision is the measure of variability between the results of sets of samples. Usually, precision increases by taking more or larger samples.

To gauge accuracy and precision, calculate the mean (average) result, standard deviation, and confidence interval.

 

Mean

 

 

Standard deviation  

 

 


Variance

This is just one example of a variance formula. Depending on the statistical methods and objectives, one of many formulas may apply.

Standard error

 

Average of the values (all the values added together and divided by the number of values).
Measure of distance between the sample mean and the true mean.
A measure of the spread between values in a group of samples.
The standard deviation of the process by which it was generated, such as the standard error of the mean or of the distribution.