There are two main approaches to analyzing data in statistics: the Data Modeling Culture and the Algorithmic Modeling Culture. Both aim to achieve two primary goals:
Prediction: Forecasting responses to future input variables
Information: Extracting insights about how nature associates response variables with input variables
Data Modeling Culture:
Approach: Assumes a stochastic data model for the "black box" that generates data
Method: Uses models like linear regression, logistic regression, and Cox model
Model validation: Employs goodness-of-fit tests and residual examination
Estimated population: 98% of statisticians
Key characteristic: Attempts to fill in the "black box" with specific statistical models
Algorithmic Modeling Culture:
Approach: Considers the inside of the "black box" as complex and unknown
Method: Focuses on finding a function f(x) - an algorithm that predicts responses y from inputs x
Techniques: Uses methods like decision trees and neural networks
Model validation: Measured by predictive accuracy
Estimated population: 2% of statisticians, with many practitioners in other fields
Key characteristic: Treats the "black box" as fundamentally unknowable, focusing on input-output relationships
Both cultures view data analysis as a process where input variables (x) enter a "black box" representing nature's functions, and response variables (y) come out. The goal is to understand or approximate this process for prediction and information extraction.
This highlights the contrasting philosophies and methodologies in statistical analysis, showcasing the traditional data modeling approach and the emerging algorithmic modeling approach. Each has its strengths and is suited to different types of problems and research goals.