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Types of Data Analysis Questions
Jeffrey Leek, Assistant Professor of Biostatistics
Johns Hopkins Bloomberg School of Public Health

Types of Data Analysis Questions

In approximate order of difficulty

  • Descriptive
  • Exploratory
  • Inferential
  • Predictive
  • Causal
  • Mechanistic

About descriptive analyses

Goal: Describe a set of data

  • The first kind of data analysis performed
  • Commonly applied to census data
  • The description and interpretation are different steps
  • Descriptions can usually not be generalized without additional statistical modeling

Descriptive analysis

Descriptive analysis

About exploratory analysis

Goal: Find relationships you didn't know about

  • Exploratory models are good for discovering new connections
  • They are also useful for defining future studies
  • Exploratory analyses are usually not the final say
  • Exploratory analyses alone should not be used for generalizing/predicting
  • Correlation does not imply causation

Exploratory analysis

Liu et al. (2012) Scientific Reports

Exploratory analysis

About inferential analysis

Goal: Use a relatively small sample of data to say something about a bigger population

  • Inference is commonly the goal of statistical models
  • Inference involves estimating both the quantity you care about and your uncertainty about your estimate
  • Inference depends heavily on both the population and the sampling scheme

Inferential analysis

Correia et al. (2013) Epidemiology

About predictive analysis

Goal: To use the data on some objects to predict values for another object

  • If $X$ predicts $Y$ it does not mean that $X$ causes $Y$
  • Accurate prediction depends heavily on measuring the right variables
  • Although there are better and worse prediction models, more data and a simple model works really well
  • Prediction is very hard, especially about the future references

Predictive analysis

Predictive analysis

About causal analysis

Goal: To find out what happens to one variable when you make another variable change.

  • Usually randomized studies are required to identify causation
  • There are approaches to inferring causation in non-randomized studies, but they are complicated and sensitive to assumptions
  • Causal relationships are usually identified as average effects, but may not apply to every individual
  • Causal models are usually the "gold standard" for data analysis

Causal analysis

van Nood et al. (2013) NEJM

About mechanistic analysis

Goal: Understand the exact changes in variables that lead to changes in other variables for individual objects.

  • Incredibly hard to infer, except in simple situations
  • Usually modeled by a deterministic set of equations (physical/engineering science)
  • Generally the random component of the data is measurement error
  • If the equations are known but the parameters are not, they may be inferred with data analysis

Mechanistic analysis

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