Exploratory Data Analysis, or EDA, is the first careful look at a dataset before formal modeling or hypothesis testing. It helps you summarize what the data look like, find unusual values, and choose useful graphs. EDA matters because real data are often messy, with missing entries, measurement errors, skewed distributions, and hidden patterns.
A good EDA can prevent misleading conclusions later.
Key Facts
- Mean = (sum of all values) / n
- Median is the middle value after sorting the data from least to greatest.
- Range = maximum value - minimum value
- Interquartile range: IQR = Q3 - Q1
- A common outlier rule is value < Q1 - 1.5IQR or value > Q3 + 1.5IQR.
- Correlation r measures the strength and direction of a linear relationship, with -1 <= r <= 1.
Vocabulary
- Exploratory Data Analysis
- Exploratory Data Analysis is the process of summarizing and visualizing data to understand its main features before formal modeling.
- Distribution
- A distribution describes how often different values or ranges of values occur in a dataset.
- Outlier
- An outlier is a data value that is unusually far from the rest of the data.
- Scatterplot
- A scatterplot is a graph that shows paired numerical data as points to reveal relationships, clusters, and trends.
- Correlation
- Correlation is a statistic that describes how strongly two numerical variables move together in a linear pattern.
Common Mistakes to Avoid
- Using only the mean, which is wrong when the data are strongly skewed or contain outliers because the mean can be pulled toward extreme values.
- Ignoring the units and context, which is wrong because a number has little meaning unless you know what was measured and how it was collected.
- Treating correlation as causation, which is wrong because two variables can move together due to a lurking variable or coincidence.
- Deleting outliers automatically, which is wrong because outliers may represent errors, rare events, or important discoveries that need investigation.
Practice Questions
- 1 For the dataset 4, 6, 7, 7, 8, 10, 22, find the mean, median, range, and identify the value most likely to be an outlier.
- 2 A class has quiz scores 12, 15, 16, 18, 20, 21, 21, 24. Find Q1, Q3, and IQR, then use the 1.5IQR rule to check whether any score is an outlier.
- 3 A scatterplot of study time versus exam score shows an upward trend with one student who studied many hours but scored low. Explain what pattern, outlier, and possible next question an EDA should report.