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edgar experimental design generator and randomiser by james k. m. brown cereals research department, john innes centre , colney, norwich, nr4 7uh, england choose an experimental design news (16/11/04): i am currently re-writing edgar. it will produce spreadsheets in microsoft excel format. my intention is to make edgar faster, more reliable and easier to use and to make the output more convenient. i also hope, in due course, to include incomplete block designs. eventually, this will become edgar version 2. i would greatly appreciate feedback on the new programmes. more news (2/3/05): the fortran routines which the old edgar programmes use are about to be taken off our server. please use the new programmes where available. i am rewriting the other programmes rather slowly so if you need one desperately, please let me know and i will try to speed it up. news (13/6/05): the server which ran the old versions of the edgar programmes has now been switched off. news (23/12/05): edgar has moved. the new address is www.edgarweb.org.uk . please bookmark it for future reference. news (20/08/10): i've fixed a bug in the way that the latin squares and complete randomisation with unequal reps programmes randomised the assignment of treatments to experimental units. thanks to michael hinchcliffe for pointing it out. note that, in the documentation below, the specifics of how edgar operates are out-of-date. i'll get round to updating them sometime soon. the main thing you should know at the moment is that edgar produces spreadsheets in ms-excel 2003. what is edgar? for those from outside the jic principles of experimental design what designs can edgar handle? how large a design can edgar generate? are there any restrictions on the format of input to edgar? how can i save output from edgar? future developments acknowledgments what is edgar? edgar is a programme which generates experimental designs and randomises the position of experimental treatments in the design. version 1.0 of edgar handles a small number of relatively simple designs, but these are appropriate for many experiments of the kind done at the john innes centre. my purpose in writing edgar is to help users of the programme to design their experiments in such a way that the subsequent analysis of the data is comparatively straightforward. edgar is written so that you can access it via the internet. you will need a world-wide-web (www) browser capable of interpreting forms, such as version 2.0 or higher of netscape. this introduction is not intended to be a substitute for proper advice. it is much better to spend a little time seeking advice from a statistician about the design and analyis of your experiment beforehand than to end up with a set of data, acquired at the cost of considerable time and money, from which it is very difficult or even impossible to draw any conclusions. to quote sir r. a. fisher, "to call in a statistician after the experiment is done may be no more than asking him to perform a post-morten examination: he may be able to say what the experiment died of". for those from outside the jic the john innes centre is an internationally renowned centre for research in plant sciences, with particular strengths in molecular biology, cell biology and genetics; this should explain my choice of examples in this introduction. however, the various designs which edgar 1.0 generates are appropriate for experiments in many other subjects, and the descriptions given below should provide much of the information you need to choose an appropriate design for your work. principles of experimental design this section gives a brief overview of the principles of good experimental design. however, it is not intended to be a substitute for personal advice or for a statistics textbook. there are fuller discussions about experimental design in many books, such as chapter 6 of steel & torrie, principles & procedures of statistics: a biometrical approach . a little terminology experimental treatments are whatever it is that you control in the experiment. these might be, for example, different environmental conditions or fertiliser regimes in which plants are grown, or different media used for tissue culture. furthermore, if you are interested in how different species, or different genotypes of one species, vary in a particular character, those species or genotypes are considered to be treatments. the term factor refers to a set of different treatments. a typical experiment might investigate long and short days - two treatments within a factor of daylength - and three genotypes (i.e. treatments) within a factor of plant genotype. a unit is the part of the experiment to which each treatment is applied. for instance, units may be pots to which different fertilisers are applied or petri dishes in which you put different media or growth rooms in which you have different environmental conditions. in an experiment involving different genotypes, you obviously can't choose the genotype of any particular plant; here, the experimental unit could be considered to be the space in the growth cabinet, glasshouse or field plot in which you decide to put a particular genotype. other experiments may involve doing something to a set of different samples in order - extracting rna or scoring microscope slides, for example. the experimental unit here is the slot in the time sequence in which you work on each sample. three principles of good experimental design are replication, randomisation and blocking. replication if your experiment is intended (as many experiments are) to test whether or not a number of treatments differ in their effects on a character in which you're interested, those treatments must be applied to replicate units of the experiment. you can only show that two treatments have different mean effects on the character if you have measured several samples given the same treatment. for instance, observing that one plant of a particular genotype is more resistant to a disease than one plant of a different genotype tells you nothing about the difference between the mean disease resistance of the two genotypes; the difference you observed could have been caused by the environment or the inoculation procedure affecting the two plants differently. to make any inference about the mean difference between the genotypes, you need to test several plants of each. the more replicates you use, the smaller the differences you should be able to identify between the means for the various treatments. in some cases, you may wish to know if some treatments are more variable than others; you may be able to reach such a conclusion if you have used sufficient replicates of each treatment. randomisation the purpose of randomising an experiment is to ensure that the layout of the experiment does not consistently favour one or other treatment in such a way that your results would be misleading. for instance, suppose that you are measuring the time of flowering of plants in a growth cabinet. if you arrange the pots so that all the plants of a variety are next to one another, and you find that one variety flowers earlier than the rest, does it mean that that variety is inherently earlier-flowering, or does it mean that the light conditions in that part of the cabinet cause plants (of whatever genotype) to flower early? you can't tell from an experiment designed in this way. or suppose that you are studying the resistance of a number of plant varieties to a disease. the experiment is laid out so that one variety (a, say) is always next to another (b). both of them are highly diseased: does this mean that they are both susceptible, or that one of them is in fact more resistant but has been infected by a large number of spores from its more susceptible neighbour? again, you can't tell from this experiment. randomising the treatments in time or space is an insurance policy, to take account of variation that you may or may not know to exist

URL analysis for edgarweb.org.uk

http://www.edgarweb.org.uk//../../jic.htm
http://www.edgarweb.org.uk//#input
http://www.edgarweb.org.uk//mailto:[email protected]
http://www.edgarweb.org.uk//#ack
http://www.edgarweb.org.uk/
http://www.edgarweb.org.uk//#whatis
http://www.edgarweb.org.uk//#save
http://www.edgarweb.org.uk//#outside
http://www.edgarweb.org.uk//#future
http://www.edgarweb.org.uk//choosedesign.htm
http://www.edgarweb.org.uk//#principles
http://www.edgarweb.org.uk//#designs
http://www.edgarweb.org.uk//#size
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Domain name:
edgarweb.org.uk

Registrant:
John Innes Centre

Trading as:
John Innes Centre

Registrant type:
UK Registered Charity

Registrant's address:
Norwich Research Park
Colney Lane
Norwich
Norfolk
NR4 7UH
United Kingdom

Data validation:
Nominet was able to match the registrant's name and address against a 3rd party data source on 31-Oct-2016

Registrar:
NBI Partnership Ltd t/a NBI Partnership Ltd [Tag = NBIPARTNERSHIP]
URL: https://www.nbi.ac.uk/domain_names/naming.htm

Relevant dates:
Registered on: 25-Nov-2005
Expiry date: 25-Nov-2021
Last updated: 24-Nov-2016

Registration status:
Registered until expiry date.

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WHOIS lookup made at 14:39:24 29-Sep-2017

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SERVERS

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  ARGS edgarweb.org.uk

  PORT 43

  TYPE domain

OWNER

  ORGANIZATION John Innes Centre

TYPE
UK Registered Charity

ADDRESS
Norwich Research Park
Colney Lane
Norwich
Norfolk
NR4 7UH
United Kingdom
Data validation:
Nominet was able to match the registrant's name and address against a 3rd party data source on 31-Oct-2016

DOMAIN

  SPONSOR NBI Partnership Ltd t/a NBI Partnership Ltd [Tag = NBIPARTNERSHIP]

  CREATED 2005-11-25

  CHANGED 2016-11-24

STATUS
Registered until expiry date.

NSERVER

  PDNS1.NBI.AC.UK 149.155.192.2

  PDNS2.NBI.AC.UK 149.155.192.3

  NAME edgarweb.org.uk

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