When we do a bioassay or something else we always choose a certain number of repetions but is it possible to omit this parameter ie working without repetition or this choice will affect negatively the reliability of th results?
Usually, the repetition is good to reduce random, human, and measurement errors. But if it becomes extremely difficult or expensive, then it can be omited with taking those risk.
Every machine we use has some accuracy (say 'x' microns). If we do only 1 experiment and obtain the value as 'y' microns, the actual reading may be (x+y) microns or (y-x) microns. In order to be more close to the actual reading, we usually take a minimum trial of 3 and average the three readings. In this way, the error due to inaccuracy of the machine can be reduce to certain extent.
Me too, I believe that repetitions help us to reduce error rate and the homogeneity of conditions (soil, humidity, genetical stability,...etc) in which we work will determine if repetition is crucial or optional.
Good answers have been presented. Replication numbering at least 3 is mandatory to reduce errors and perform statistical analysis. No data can called reliable and cannot be got published in journal of repute. So there is essential requirement of repeating experiment at least thrice.
However, you can perform un-replicated experiment (if it can be called an experiment) just to get preliminary information, shortlisting of treatments, reducing number of cultivars/varieties and getting preliminary data and trends. The results will be for your own information and planning to start systematic replicated studies. These results never be submitted for publication.
That depends on your experimental design, how repeatable and reproducible your measurement method is, how precise you need the results to be, and how variable the samples are.
If you have a validated method with well characterized uncertainties then you can certainly do only one measurement (which may or may not be what you mean by "experiment") on each sample. If you have to run duplicates or triplicates on one sample as some answers have advocated then your method needs improvement.
If your measurement method has uncertainties larger then you can accept in your final result (which is the case for some bioassays) then you need to run multiple replicates on each sample.
If your samples are very variable then you may need to analyse more than one sample to get a result (and uncertainty) that is representative of a treatment group.
It is very important to keep in mind that running multiple replicates does not reduce bias.
Good answers have been given. Furthermore, if you do not consider repetitions in your study then in statistical analysis the lack-of-fit test will be SIGNIFICANT. Thus there is a need to repeat your experiments with repetitions.
Duplicating results: Science is based on many principles, including the principle of making accurate measurements which result in reproducible results. It would be unscientific to not confirm if the result was repeatable. The number of times needed to confirm a result is repeatable may vary, but if 'measured' are never less than three.
I agree with Peter. It depends on your problem. Foe example, if an experiment is a calculation, "experimental variability" is 0. So, if you have no repetition, you must have a good idea of the global uncertainty and then include it in your calculation to be able to perform statistical tests. Usually, statistical packages don't include this option, so, you have to do it "by hand"!
Replicate analysis are important to determine measurement uncertainty. As soon as you determine the measurement uncertainty of your analytical method somehow, you can. For example if you determine measurement uncertainty and accuracy during your validation procedure, you are allowed to do the analysis one time, no replicates.
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