Coefficients of Error

Formerly called the Nugget Variance, also known as the Nugget Effect, this is a measure of the intra-sectional variance.

It is a primary contributor to the Total variance. Ideally, this value will be both small and of roughly the same magnitude as the Variance of Systematic Random Sampling.

If this value is too high, more sampling needs to be performed on each section.

Measure of the inter-sectional variance.

It is a primary contributor to the Total variance. Ideally, this value will be both small and of roughly the same magnitude as the Variance due to noise.

If this value is too high, more sections need to be sampled. At least three sections must be sampled for this value to be statistically meaningful. If less than three sections are displayed, these values are not shown in the results. Although this value depends to some degree upon the Variance due to noise, this dependency will normally be very small if the Variance due to noise is reasonably small.

Sum of the Variance due to noise and the Variance of Systematic Random Sampling over all the sections sampled.

This value is the primary contributor to the final Coefficient of error for a given estimate over a series of sections.

At least three sections will need to be sampled for this value to be statistically meaningful.

Estimated precision of the population size estimated using Optical Fractionator. At least three sections must be sampled for this value to be computed.

Mean number of cells per counting frame across one or more sections.

Estimated variance of the number of cells per counting frame over one or more sections.

This is an indicator of how evenly the cell count was distributed over the counting frames.

Estimated variance of the estimated mean number of cells per counting frame from the true mean.

A sufficient number of counting frames should be used to ensure that this number is small.

Inverse of the fraction of the total area actually sampled.

[Contour area or Counting frame area] * [Estimated mean cell count]

This is a simple linear, biased, approximation.

Variance of the population size estimated using the linear approximation formula for Estimated cell population count.

Alternative method of estimating the CE for random populations. The calculations for this estimated CE are not unbiased if the population distribution is non-random, or if the contours circumscribing the regions of interest are not accurately traced.