itfitzme
VIP Member
The central limit theorem is foundation to statistics. It shows that the sample of means has a variance that is far less than the variance of the sample itself. The variance of the sample of means is S/sqrt(n). where S is the variance of the total population. Because of the central limit theorem, sample data can be reliably used to determine the variance and mean of the underlying population.
Global temperature data is, by it's nature, a sampling of the underlying population of global temperature. Because of the central limit theorem, the global temperature record can be accurately assessed.
The central limit theorem is responsible for the IPCC use of terms "likely" and "highly likely". These terms have numerical values attached to them that are specific to the p-value of the statistical tests.
Global temperature data is, by it's nature, a sampling of the underlying population of global temperature. Because of the central limit theorem, the global temperature record can be accurately assessed.
The central limit theorem is responsible for the IPCC use of terms "likely" and "highly likely". These terms have numerical values attached to them that are specific to the p-value of the statistical tests.