Many statistical tools we use are just correlational. The fundamental dilemma of data analysis in social science is that we essentially want to make causal statements in the absence of randomized experiments. However, unlike in natural science, it is not easy to conduct randomized experiments in social science because of ethical and practical reasons. It is known that the best way to investigate causal relationship is to conduct randomized experiments. “In nonrandomized experiments, it is usually only possible to determine the existence of a relationship between two measurements, but not the underlying mechanism or the reason for it.” mentioned a very important statement that we should keep in mind when we use data science to solve social problems:.In other words, we should say what we can say, not what we want to presents a table showing common mistakes Seek to show that changing one measurement always and exclusively leads to a specific, deterministic behavior in another(인과, e.g., wing design)įrom Leek & Peng main point is that we should keep in mind the type of question being asked by our own data analysis. 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) Predict(예측) another measurement (the outcome) on a single person or unit(e.g., prediction of how people will vote in an election)

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) Search for discoveries(발견), trends, correlations, or relationships between the measurements to generate ideas or hypotheses(e.g., The four-star planetary system Tatooine) Seek to summarize(요약) the measurement in a single data set without further interpretation(e.g., US Census) Before talking about R and social problems, let’s talk about the types of data analysis 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.”

P-hacking versus data dredging