In light of this article Data Science Has Become About Lending False Credibility To Decisions We've Already Made published in Forbes, I would appreciate input from the statistical and data science community:
1. What can be done to ensure credibility in findings based on machine learning and big data?
2. Is hypothesis testing inherently superior and more credible than machine learning?
The article begins with:
One of the greatest failures of data science (...) [i]t no longer matters what our data actually says or whether the data we are using is in any way relevant to the questions we ask of it. All that matters is that we can justify our preordained decisions with the certainly of “data.”
As we rapidly undermine the promise of data science, will our trust in data fade with it?
[O]ur era of searching data for answers has devolved into searching data until we find support for the answer we've already decided upon.
It concludes saying:
Putting this all together, data science is no longer about analyzing data or giving our data the opportunity to speak to us.
Most dangerously, it has become about the misuse of statistics, data, research methodologies and the scientific method to lend false credibility to decisions that have already been made.
We no longer devise a hypothesis and test it using data. We start with the conclusion we want and find the data and methods to support it.