The design of the design of experiments
Maybe a more accurate title for this piece would be ‘the design of the designer of experiments’, but that is not quite right either. The story is actually one of evolution through a series of chance happenings, though I hope the result is intelligent design as far as the experiments are concerned.
We all do experiments all the time – try something, see what happens and learn something in the process. That is not good enough for science. As Richard Feynman said, “The first principle is you must not fool yourself, and you are the easiest person to fool”. So, scientists developed principles and tools for designing experiments that would be robust against biases and allow the level of certainty, or uncertainty, of results to be quantified. No more fooling. These principles were written down by R A Fisher in his 1925 book Statistical Methods for Research Workers, and that book has been the basis of experimental design for the research areas in which I work – mainly agriculture, ecology and environmental science – ever since.
However, things have been changing, and I was prompted to reflect on these changes as I moved books on experimental design from my old house and took them to the Stats4SD office. Arranging them on a shelf, I realised they represented in part the way my thinking and understanding has evolved during the 40+ years I have been helping researchers design experiments.
I did have a copy of Fisher (1925), but gave it away years ago, so at the left-hand side of my shelf are some of the books firmly aligned with the Fisher tradition. Finney’s book was the first from which I started to grasp theory. Cox made it all seem so simple. Box, Hunter and Hunter had the whole subject tidied up with clever optimal designs for many situations. Kempthorne derived the almost mystical connection between randomisation - and model-based analyses, though I never really understood why it happens. Cochran and Cox presented a catalogue of designs into which researchers seemed to fit any problem, and Gomez and Gomez gave recipes for the design and analysis of, apparently, any useful design. These two books were the standard references for thousands of agricultural scientists around the world.
The next group of books represents the beginning of a shift away from starting with a theory of design and making the research question fit it, to acknowledging that real research problems and contexts rarely allow those neat designs in the catalogues to be used. The sizes of blocks are not all equal, the number of varieties is not a perfect square, not all ewes have two lambs. Roger Mead’s book emphasised how a good experimental design could be chosen based on principles, even if there was no exact theory or the design could not be shown to be optimal. I had the privilege of being taught by Roger Mead and those messages still underlie much of how I think about experimental design. Since then, I have also understood something represented by books, such as those on experimental with perennials, experiments for tree improvement and ecosystem experiments. The application area matters. All the design theory and principles will not help you design a good tree trial if you don’t understand something about trees: how they grow, how they are managed and measured, and what experimenters are really trying to find out about them. Somewhere I lost my copy of Robinson’s Practical Strategies for Experimenting, a helpfully practical book which started with trying to place experiments within a larger context of how research works, for example by asking whether a designed experiment is actually needed.
When the participatory principle became a standard part of much agricultural and environmental research, experimental design had to be adapted to the fact that people participating did not always have the same objectives as a researcher. In this framework, experiments have multiple purposes and designs to trade-off multiple interests and concerns. Most importantly, designs have to be negotiated between interested parties. Some principles long assumed to be fundamental to experimental research, such as random allocation of treatments to units, might need to be questioned. For example, why exactly do we need to randomise in this situation and what will be lost if we don’t? Might the errors and confusion caused by trying to randomise or blind a trial be a bigger threat to validity than using a systematic layout that is assumed to be ‘as good as random’?
At the righthand side of that shelf are a few books that are products of some of the research in which I have been involved. They are full of discoveries, insights, new theory and new practices. In almost every case, these are not based on the results of an experiment, but from assembling the results of many experiments and other non-experimental studies. This assembling of facts is done retrospectively, but can we design experiments that might make it more efficient? The standard approaches to experimental design, that we still teach and expect agricultural scientists to understand, are based on the notion there is ‘an effect’ of treatments, which can be measured by differences in response between two or more treatment groups. The experiment is designed to estimate that effect, or test hypotheses about it. But in applied sciences, like agriculture and much of ecology, we know there is not ‘an effect’, but a complex pattern of responses that depend on conditions and context. This is true when we measure a biophysical response such as crop yield, and even more so when we measure a human and subjective response, such as opinions about the crop. We can design experiments that can help us understand what is happening in these situations. They incorporate elements of design of non-experimental and may be limited by what can be deduced from observational studies, and they can look very different from the designs in those classic books.
Over the last few weeks, I have been working with a network of farmers, development NGOs and researchers who are setting up a study with up to 1000 people who are learning about principles and practices for improved making and use of compost. They will be setting up an experiment with all participants making compost in two or more ways, each based on their own choices, and evaluating their effects on soils and crop production. The design is being negotiated to match the aims, interests, possibilities and constraints of all those involved. I think we have come up with something that is feasible, useful and perhaps new. What we don’t have is the theory and sets of derived principles on which it is based. We are still waiting for someone to write that book.
Author: Ric Coe
Ric’s main focus is on improving the quality and effectiveness of research for development using the application of statistical principles and ideas. He is particularly interested in research design, including the design of complex integrative research projects.
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