In exploratory data analysis (ED

The main goal of Exploratory Data Analysis (EDA) is to visually and statistically summarize the data in order to understand its structure, patterns, and anomalies. By applying various visualization techniques (such as histograms, scatter plots, and box plots) and descriptive statistics (like mean, median, and standard deviation), EDA helps data analysts gain insights into the relationships and trends within the dataset before performing more formal statistical modeling or hypothesis testing. EDA is used to identify missing data, detect outliers, and explore the distribution of variables. It does not aim to test hypotheses or build models directly; instead, it prepares the data for such further analysis. Why Other Options Are Incorrect: • A: Testing the statistical significance of a dataset is typically done after performing EDA, especially in hypothesis testing or when applying inferential statistics. • C: Developing predictive models happens after understanding the data through EDA. Models are created based on insights gained from exploratory analysis. • D: Reporting findings is a task typically performed at the end of the data analysis process, not at the start during EDA. • E: Ensuring the data adheres to industry standards is more related to data quality and preparation than to the purpose of EDA, which is focused on exploration and analysis.