Hostname: page-component-848d4c4894-75dct Total loading time: 0 Render date: 2024-05-18T18:41:39.735Z Has data issue: false hasContentIssue false

The Experimental Error of Field Trials

Published online by Cambridge University Press:  27 March 2009

W. B. Mercer
Affiliation:
(Vans Dunlop Scholar, University of Edinburgh),
A. D. Hall
Affiliation:
(Vans Dunlop Scholar, University of Edinburgh),

Extract

The magnitude of experimental error attaching to one or more field plots is a question of extreme importance in Agricultural Science, because upon its proper recognition depends the degree of confidence which may be attached to the results obtained in field work. A very cursory examination of the results of any set of field trials will serve to show that a pair of plots similarly treated may be expected to yield considerably different results, even when the soil appears to be uniform and the conditions under which the experiment is conducted are carefully designed to reduce errors in weighing and measurement.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1911

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Page 115 note 1 As the number of plots decreases the value which can he attached to the determination of the S. D. diminishes, and in the later calculations when small numbers of plots are made up from scattered units, the results given are invariably averages of two or three determinations.

Page 124 note 1 Group the plots as in construction of a frequency curve. Select the group in which the mean is expected to lie (any other group may be taken without altering the result but the arithmetic is generally less arduous if the origin is taken near the mean) and from this as origin index the groups positively and negatively. Multiply the number in each group by the index number of the group. Sum and divide by the total number of observations. This gives v1. Next multiply the number in each group by the square of the