In the following, “study” will be used to stand for experiments, trials, studies, surveys, and so forth.

Here are some basic design principles to keep in mind:

First, use the simplest design possible that will still answer your research questions.  There are many academically fascinating experimental designs that are a nightmare to set up, administer, carry out, and then analyze.  Simple designs usually have simple statistical analysis methods associated with them that are more robust to problems such as missing data or weird data distributions.

In contrast, more complex designs can create real problems in the statistical analysis when things go wrong.

For example, in a design that has a complex structure, it may turn out that due to physical reality that some treatment combinations cannot actually be studied together.  In such cases the damage to the design may be so total as to make most of the data worthless.

Second, make sure that the design can actually produce data that has a chance of answering your research questions.  The statistical power of the design is one quality that can be examined for this; but, more fundamentally, you need to make sure that the data can even logically bear on your research question!

Use a design that can be collapsed onto itself if necessary.

You may need to collect data on multiple variables.  For example, a nutritional study may collect a complete lipid profile for each subject in a cross-over design, along with hormone measurements at sparser time points, plus information on diet and mood, along with the usual demographic and baseline characteristics.

Avoid designs that have complex structure such as balanced incomplete blocks, or heavily aliased fractional factorial designs, or the like, unless your study can be replicated with little investment of time and expense.