4 Common Data Analysis Mistakes to Watch Out For

By datasciencecentral - 2021-03-16

Description

Statistical analysis is poorly misunderstood by many, resulting in array of problems. There are many ways to ruin your data analysis. 4 of the less well-known…

Summary

  • Statistical analysis is poorly misunderstood by many, resulting in array of problems.
  • The following four categories are surprisingly common analysis pitfalls--even in peer-reviewed, published research.
  • However, if you were to compare correlations to zero with Pearson's r [no term], it's possible to find that one group has a statistically significant correlation while the other does not.
  • Also consider replication studies to confirm your findings.

 

Topics

  1. Backend (0.2)
  2. NLP (0.15)
  3. Stock (0.14)

Similar Articles

15 Essential Steps To Build Reliable Data Pipelines

By Medium - 2020-12-01

If I learned anything from working as a data engineer, it is that practically any data pipeline fails at some point. Broken connection, broken dependencies, data arriving too late, or some external…