Why is falsifiability important in quantitative research?

Answer 1

Falsifiability means that there is a way to demonstrate that the hypothesis, and therefore the null hypothesis, could be wrong. There has to be a chance that a hypothesis could be wrong, otherwise statistical tests based in the probability that our claims are right cannot be conducted. It's one of those reverse truth situations -- if something could be true, it also must be able to be false.

Quantitative research is based on the idea that we little is proven or in other words that there are few things that are in fact 100% Truth. Researchers talk about this as "Big T- truth". Few things can be proven without a doubt, when they are we treat them as Truth.

For example, even though it is a true that gravity exists under the right parameters, it can also be proven that under other parameters this is a falsehood (think of how planes fly). Despite our knowledge of gravity, we can still show evidence (or specific situations) when it is false to claim that gravity is 100% always true.

In fact, sometimes we can create situations under certain conditions when we can defy (or falsify) this truth. As long as hypotheses have the chance of being false, we can conduct research to determine the probability that our hypotheses might be true.