As many recent articles have pointed out, much scientific research is not reproducible, which poses a troubling problem for the scientific field. Research that can’t be reproduced can lead to the waste of millions of tax payers’ dollars when scientists chase after a hypothesis for which they think they have gathered good evidence to support, but which later doesn’t pan out. It can lead to people excitedly jumping on the next “miracle” cure. It can lead to the disgraceful ruin of careers, when scientists must retract their work.
I will not deny those very negative consequences; however, I found Jacob Hovarth’s article in “Scientific American” added a much needed optimistic perspective to the discussion.
Given our limitations as human beings, absolute truth is elusive. We will never be able to discover Truth definitively. At best, we can study and observe the world around us, and based on those observations come up with explanations, which we test again and again, looking for flaws in our proposed explanation. Most scientists are taught at some point that it is impossible to “prove” a hypothesis. Instead we gather evidence to suggest that our hypothesis is correct. Even after we gather some evidence in support of a hypothesis, it is always possible that we will come across some additional findings that capture some different aspect of the truth and that run counter to the predictions of that hypothesis.
As Hovarth points out, whether new evidence supports or contradictions earlier evidence, we learn something either way. Except in the case of out-right fraud or data manipulation (which, in my opinion, are separate issues), contradictory evidence does not negate the value of pervious findings. And in many cases, the search for an explanation for discrepancies between results can lead to important discoveries that might not have been explored otherwise.
The scientific field should put mechanisms in place to incentivize researchers to conduct replication experiments. At face value, these experiments may seem like a pointless use of money and resources, but they could in the long-run make research more efficient. For example, if replication experiments with a bacteria end up revealing that some labs are unknowingly using a strain with a mutation in a virulence gene, future researchers will ensure they work with the correct strain and cease wasting money conducting experiments in a strain that lacks clinical relevance.