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Key Differences between Exploratory Data Analysis and Confirmatory Data Analysis

Exploratory data analysis involves analyzing data to discover patterns, trends, and insights without preconceived hypotheses. It focuses on uncovering hidden relationships in the data and exploring multiple potential outcomes.

On the other hand, confirmatory data analysis aims to test specific hypotheses that are formulated before conducting the analysis. It involves statistical testing and hypothesis validation to confirm or refute the proposed theories based on the data.

Key differences between exploratory data analysis and confirmatory data analysis:

• EDA is more exploratory and flexible, while CDA is more structured and hypothesis-driven.
• EDA is used for generating hypotheses and insights, while CDA is used for hypothesis testing and validation.
• EDA involves data visualization and summary statistics to explore data, while CDA focuses on statistical tests and model validation.
• EDA is often used in the initial stages of data analysis, while CDA is typically used to confirm findings and make data-driven decisions.

Both approaches play essential roles in the data analysis process, with EDA laying the foundation for CDA by providing insights and hypotheses to be tested rigorously in confirmatory analysis.

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