Double-Blind Experiments
In the example above, it was explained that neither the subjects nor the researchers knew who was
receiving which dosage, or the placebo. A study is said to be double-blind when neither the subjects (or
experimental units) nor the evaluators know which group(s) is/are receiving each treatment or control.
The reason for this is that, on the part of subjects, simply knowing that they are part of a study may affect
the way they respond, and, on the part of the evaluators, knowing which group is receiving which
treatment can influence the way in which they evaluate the outcomes. Our worry is that the individual
treatment and control groups will differ by something other than the treatment unless the study is double-
blind. A double-blind study further controls for the placebo effect.
Randomization
There are two main procedures for performing a randomization . They are:
• Tables of random digits . Most textbooks contain tables of random digits. These are usually tables
where the digits 0, 1, 2, 3, 4, 5, 6, 7, 8, and 9 appear in random order (well, as random as most things
get, anyhow). That means that, as you move through the table, each digit should appear with probability
1/10, and each entry is independent of the others (knowing what came before doesn’t help you make
predictions about what comes next).
• Calculator “rand” functions. The TI-83/84 calculator has several random functions: rand, randInt
(which will generate a list of random integers in a specified range), randNorm (which will generate
random values from a normal distribution with mean μ and standard deviation σ ), and randBin (which
will generate random values from a binomial distribution with n trials and fixed probability p —see
Chapter 10 ). If you wanted to generate a list of 50 random digits similar to the random digit table
described above, you could enter randInt(0,9) and press ENTER 50 times. A more efficient way
would be to enter randInt(0,9,50). If you wanted these 50 random integers stored in a list (say L1),
you would enter randInt(0,9,10) → L1 (remembering that the→ is obtained by pressing STO ).
Digression: Although the calculator is an electronic device, it is just like a random digit table in that, if
two different people enter the list in the same place, they will get the same sequence of numbers. You
“enter” the list on the calculator by “seeding” the calculator as follows: (Some number) → rand (you
get to the rand function by entering MATH PRB rand) . If different people using the same model of
calculator entered, say, 18 → rand , then MATH PRB rand , and began to press ENTER repeatedly, they
would all generate exactly the same list of random digits.We will use tables of random digits and/or the calculator in Chapter 9 when we discuss simulation.Randomized Block Design
Earlier we discussed the need for control in a study and identified randomization as the main method to
control for lurking variables—variables that might influence the outcomes in some way but are not
considered in the design of the study (usually because we aren’t aware of them). Another type of control
involves variables we think might influence the outcome of a study. Suppose we suspect, as in our
previous example, that the performance test varies by gender as well as by dosage level of the test drug.
That is, we suspect that gender is a confounding variable (its effects cannot be separated from the effects
of the drug). To control for the effects of gender on the performance test, we utilize what is known as a
block design . A block design involves doing a completely randomized experiment within each block. In
this case, that means that each level of the drug would be tested within the group of females and within the