Probably the worst error is thinking there is a correlation when that correlation is purely artificial. Take a data set with 100,000 variables, say with 10 observations. Compute all the (99,999 * 100,000) / 2 cross-correlations. You are almost guaranteed to find one above 0.999. This is best illustrated in may article How to Lie with P-values (also discussing how to handle and fix it.)
This is being done on such a large scale, I think it is probably the main cause of fake news, and the impact is disastrous on people who take for granted what they read in the news or what they hear from the government. Some people are sent to jail based on evidence tainted with major statistical flaws. Government money is spent, propaganda is generated, wars are started, and laws are created based on false evidence. Sometimes the data scientist has no choice but to knowingly cook the numbers to keep her job. Usually, these “bad stats” end up being featured in beautiful but faulty visualizations: axes are truncated, charts are distorted, observations and variables are carefully chosen just to make a (wrong) point.
Related articles
- How to Lie with P-values
- Four Types of Data Scientist
- Debunking Forbes Article about the Death of the Data Scientist
- Why You Should be a Data Science Generalist – and How to Become One
- Becoming a Billionaire Data Scientist vs Struggling to Get a $100k Job
- Is a PhD helpful for a data science career?
- If data science is in demand, why is it so hard to get a job?
- Why do people with no experience want to become data scientists?
- Why is Becoming a Data Scientist so Difficult?
- Full Stack Data Scientist: The Elusive Unicorn and Data Hacker
- Statistical Significance and p-Values Take Another Blow
- Are data science or stats curricula in US too specialized?
- How do you identify an actual data scientist?
- Is it still possible today to become a self-taught data scientist?
- Will the job outlook for data scientists severely decline after 2020?
- Why Logistic Regression should be the last thing you learn
Source for picture: here
