# Chapter 1 Introduction to data analysis

## 1.2Types of Data Analysis

• ##### Leek & Peng(2015) categorized data analysis into the 6 types as presented in the table below, and emphasized “mistaking the type of question being considered is the most common error in data analysis.”
Types of Data Analysis Questions being asked
Descriptive data analysis(기술통계분석) seek to summarize(요약) the measurement in a single data set without further interpretation(e.g., US Census)
Exploratory data analysis(탐색적자료분석) search for discoveries(발견), trends, correlations, or relationships between the measurements to generate ideas or hypotheses(e.g., The four-star planetary system Tatooine)
Inferential data analysis(추론분석) quantify whether an observed pattern will likely hold beyond the data set in hand or in population(모집단)(e.g., a study of whether air pollution correlates with life expectancy in US)
Predictive data analysis(예측분석) predict(예측) another measurement (the outcome) on a single person or unit(e.g., prediction of how people will vote in an election)
Causal data analysis(인과관계분석) seek to find out what happens to one measurement on average if you make another measurement change(인과, e.g., causal relationship between smoking and cancer)
Mechanistic data analysis(결정론적관계분석) seek to show that changing one measurement always and exclusively leads to a specific, deterministic behavior in another(인과, e.g., wing design)
• ##### Leek & Peng(2015) presents a table showing common mistakes
Real Question Type Perceived Question Type Phrase Describing Error
Inferential Causal Correlation does not imply causation
Exploratory Inferential Data Dredging (or p-hacking)
Exploratory Predictive Overfitting(과적합)
Descriptive Inferential n of 1 analysis