What are the key differences between exploratory data analysis (EDA) and confirmatory data analysis (CDA)?

Key Differences Between Exploratory Data Analysis (EDA) and Confirmatory Data Analysis (CDA)

Exploratory Data Analysis (EDA) involves the initial investigation of data to discover patterns, identify anomalies, and test hypotheses, without any preconceived notions. It focuses on exploring the data and generating insights through visualizations and summary statistics.

On the other hand, Confirmatory Data Analysis (CDA) is a more structured and hypothesis-driven approach where specific hypotheses are tested using statistical methods. CDA aims to confirm or refute existing theories or hypotheses based on the data collected.

Some key differences between EDA and CDA include:

• Goal: EDA aims to explore and understand the data, while CDA aims to confirm or refute specific hypotheses.
• Flexibility: EDA is flexible and allows for open-ended exploration, while CDA is more rigid and follows a pre-defined plan.
• Tools: EDA involves tools like data visualization and summary statistics, while CDA involves hypothesis testing and statistical modeling.

Both EDA and CDA are important stages in the data analysis process, with EDA typically preceding CDA to inform the development of hypotheses and guide the analysis process.