Types of Failure
In making any knowledge claim (including the claim that something is unknowable) there are two kinds of errors we can make. Methodologists call these Type I and Type II errors. It doesn’t matter whether the source of a knowledge claim is intuition, deductive, or empirical.
Skepticism
At least since the time of the ancient Greeks, the essence of scholarship has been skepticism. There are two kinds of skepticism: (1) False positive skepticism, and (2) False negative skepticism. A false positive skeptic is afraid of making knowledge claims that aren’t correct. A false negative skeptic is afraid of discarding ideas that might have value. A false positive skeptic is ready to discard an idea given a small amount of contradicting evidence. A false negative skeptic requires overwhelming contradicting evidence before giving up on an idea.
The two forms of skepticism are evident in everyday statements such as the following:
False Positive Skeptic: “You don’t know that for sure.” “I really doubt that that’s useful.” “There’s no way you could ever know that.” “There’s not enough evidence for me to believe that.”
False Negative Skeptic: “It might well be true.” “It could yet prove to be useful.” “We might know more than we think.” “There’s not enough evidence for me to stop believing …”
In short, the two forms of skepticism might be summarized by the following contrasting assertions:
False-Positive Skeptic: “There is insufficient evidence to support that.” False-Negative Skeptic: “There is insufficient evidence to reject that.”
There is nothing inherently superior about one form of skepticism compared with the other. All of us rely on both types of skepticism in our lives.
People who engage in research are optimists, since they must think that through their own actions it may be possible to acquire new knowledge. People who are pessimistic about knowledge claims typically don’t engage in research, since they are skeptical about the possibility of generating new knowledge.
Since researchers are typically hopeful and optimistic, the principal danger in empirical research is our own eagerness to discover something. For this reason, active researchers are more likely to make Type I errors (i.e., claiming something to be true that is not true/useful/knowable) than Type II errors. Consequently, throughout the history of empirical research, the principal concern has been to minimize or avoid Type I errors.
As we will see later, when research involves quantitative data, it is often possible to estimate the probability of making a Type I or Type II error. Although modern science is several hundred years old, the ability to measure the probability of error is less than a century old, and is one of the most important advances in modern empirical methodology.
Recap
What’s a Type I Error? Wrongly claiming something to be true, useful or knowable. What’s a Type II Error? Wrongly claiming something to be false, useless or unknowable. In quantitative empirical research, it is often possible to estimate the probability of making either error.
“But as for certain truth, no man has known it Nor will he know it; neither of the gods, Nor yet of all the things of which I speak. And if by chance he were to utter The final truth, he would himself not know it: For all is but a woven web of conjectures.”
- Xenophanes