For experimenters, stopping rules can make or break the validity of statistical tests. They seem simple, however, we’ve all come across misconceptions about statistics that can alter the interpretation of the results. Here, we’ll dig deeper into the rules we follow within null hypothesis significance testing (NHST), and why.
If you missed Spark Summit, catch this talk from Abdulla about how to build predictive recommendation models and ensure type safety using Apache Spark DataFrames. See how Credit Karma's choice of metric evaluation helps us calibrate models to obtain the best global result, and hear our lessons learned when we scaled our model development environment to handle Terabyte-scale data with thousands of features.