There is also some evidence to suggest that the likelihood of accidents is increased in pilots that are colour deficient (Vingrys & Cole, 1986).
So what?
There is also some evidence to suggest that the likelihood of accidents is not increased in pilots that are colour deficient (see: decades and tens and thousands of hours of real life operations).
Other studies have shown that subjects with colour vision deficiencies make more errors and are slower in recognising aviation signals and colour coded instrument displays (Vingrys & Cole, 1986; Cole & Maddocks, 1995; Squire et al, 2005).
Those studies don't "show" that CVD pilots make more errors and are slower than non-CVD pilots, in real-life operations.
There are also a small number of tasks in which colour information is not used redundantly and therefore the correct interpretation of colour signals becomes very important.
What are those tasks and where is the high level evidence to prove that pilots with CVD do not perform them as effectively as pilots without CVD in real life operations? The high level evidence is to the contrary.
My favourite paper is David H. Freedman's
Wrong: Why Experts Keep Failing Us—And How to Know When Not to Trust Them. My favourite quotes from that paper:
“Researchers headed into their studies wanting certain results—and, lo and behold, they were getting them.”
“At every step in the process, there is room to distort results, a way to make a stronger claim or to select what is going to be concluded,”
“Not surprisingly, the studies that tend to make the grade are those with eye-catching findings. But while coming up with eye-catching theories is relatively easy, getting reality to bear them out is another matter. The great majority collapse under the weight of contradictory data when studied rigorously.”
“[R]esearchers were frequently manipulating data analyses, chasing career-advancing findings rather than good science ...”
“[A]ssuming modest levels of researcher bias, typically imperfect research techniques, and the well-known tendency to focus on exciting rather than highly plausible theories, researchers will come up with wrong findings most of the time.”
“Simply put, if you’re attracted to ideas that have a good chance of being wrong, and if you’re motivated to prove them right, and if you have a little wiggle room in how you assemble the evidence, you’ll probably succeed in proving wrong theories right.”