What is the difference between exploratory data analysis and confirmatory data analysis?

The Difference Between Exploratory Data Analysis and Confirmatory Data Analysis

Exploratory Data Analysis (EDA) and Confirmatory Data Analysis (CDA) are two approaches used in the field of data analysis to derive insights and draw conclusions from data. Here's a breakdown of their main differences:

Exploratory Data Analysis (EDA)

• EDA is primarily used to uncover patterns, trends, and relationships in the data without making any assumptions beforehand.
• It involves visualizing data using graphs, charts, and summary statistics to gain a better understanding of the dataset.
• EDA is more flexible and allows for exploration of multiple hypotheses without a predefined research question.
• It is typically the first step in the analysis process to guide further investigation.

Confirmatory Data Analysis (CDA)

• CDA, on the other hand, is used to test specific hypotheses or theories that are formulated based on prior knowledge or assumptions.
• It involves hypothesis testing, model building, and validation using statistical methods.
• CDA is more structured and follows a predefined research plan to confirm or reject hypotheses.
• It focuses on validating existing theories or models rather than exploring new ones.

In summary, EDA is about exploration and discovering insights in the data, while CDA is about confirmation and testing predefined hypotheses. Both approaches play a crucial role in data analysis, depending on the research objectives and goals.