From Table \ref{618809} it can be seen that if a measurement is taken using the rapid splitting test (which is currently being used in early selection breeding programs) a hypothetical repeated measurement on the same sample with the same cut orientation would provide a result within \(\pm417u\epsilon\) 95% of the time. Further, if the test was conducted at a random cut orientation, any given result would (95% of the time) be within \(\pm1006u\epsilon\) \(\left(417+589\right)\) of mean of the hypothetical distribution created by testing the same sample at random orientations an infinite number of times.
These results suggest that using splitting tests on a breeding population such as the one presented in Table \ref{929591} have the potential to remove poor performing individuals as part of a non-intensive breeding selection, for example taking the top 25% of the population would likely remove the bottom 20% going forward. However the limited resolving power of the splitting tests precludes the ability to select the "best" individual or even the top few percent individuals. Current early selection programs utilizing these tests aim to remove the poorest performing individuals in order to reduce the expense of further more extensive breeding programs. These tests are suitable for this purpose, however without further development accurately ranking individuals for selection is problematic. Note that these estimates are based on the population presented in Table \ref{929591}. Populations with significantly different means or variances would yield the testing more or less worth while.
Figure \ref{273819} shows a simplistic visual representation of selecting the top 25% of individuals (in red), generated by ordering the rapid splitting test data from lowest to highest. However using the results in Table \ref{618809} a representation of the 'true' values can be seen in Figure \ref{920901}, (note these are simply created by adding a value randomly sampled from a normal distribution characterised by a mean of 0 and a standard deviation of 513 from the 95% confidence interval \(\pm1006u\epsilon\), the real value is unknown). When the same 'true' values are reordered from lowest to highest, with the same point color as in Figure \ref{109992} it can be seen some poorer individuals are included and some better individuals are missed because the inaccuracies within the test.
Conclusion
A method was presented which enabled the estimation of the precision of the destructive growth strain measurements obtained by the unrepeatable splitting tests. The results showed that within a population with a mean of \(1807u\epsilon\) and a standard deviation of \(662u\epsilon\) a rapid splitting test result has a 95% confidence interval of \(\pm1006u\epsilon\). Splitting tests show marginal suitabllity for use in early selection breeding programs where identifying the best individual is not of high concern, rather removing the worst individuals to make future programs more cost effective is. However the splitting tests were shown not to provide high enough precision to be used in intensive selections due to their measurement errors.