Planning statistical research
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Most scientific work starts with a question about the world we live in. For a statistician, these questions can be classified into a few different kinds:
- We can start with questions about a single Unit, like a biological organism, a manufactured product, a plot of ground, a city block or any of many different things we want to understand. In this type of study, we are often concerned with the dynamics of the Unit; how does it vary in time? Can we identify what aspects seem to control others? (See research subject.)
- Sometimes, our interest in a Unit may be purely instrumental, that is, we are interested in the Unit because of a population to which it belongs or an environment in which it resides. Our desire to describe a population may be satisfied by a statistical report on a sample from that population. The methodology is to select a sample and observe just the Units which are selected and then to summarize our results and interpret their meaning for the population. When we deliberately manipulate something in the process of observation, we call it an experiment. When we attempt to observe the Units without affecting them, we call it a survey.
- Sometimes, we want to understand the internal workings of a Unit so we look at its components and how they relate to each other. Physiology is an example of a discipline in which this kind of focus is common.
In much research, we use all three modes at different stages. A useful synthesis of this kind of thinking was provided by Ludwig von Bertalanffy.
In every type of research, we must be concerned with managing the observational error that is inherent in all empirical research. We can increase the precision of our research by
- using a more precise instrument (e.g., a more powerful telescope)
- increasing the number of observations so the constant we try to measure stands out better against the noise.
- changing the design of our research. This last approach can become very technical, so we will postpone its discussion until optimum experimental design.
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