Welcome back to Analyzing Data with Box Plots! In the first lesson of this course, you learned how to read a box plot — locating the minimum, $Q_1$, median, $Q_3$, and maximum on the number line. That skill is essential, but knowing where the numbers sit is only half the story.

In this second lesson, we shift from reading to interpreting. Our focus is on what each part of the box plot actually tells us about the data. By the end, you will be able to explain what the box, the median line, and the whiskers reveal about center, spread, and the overall shape of a distribution.

Think of reading a box plot like reading the temperature on a thermometer: you get a number, but you still have to decide whether that number means "bring a jacket" or "grab sunscreen." In the same way, once we read that $Q_1 = 18$ and $Q_3 = 34$, we need to think about what that for the data.

The box stretches from $Q_1$ to $Q_3$, so it captures exactly the middle 50% of all data values. This is one of the most important takeaways in box plot interpretation: half of the entire dataset lives inside that rectangle.

The width of the box equals the IQR ($Q_{}$). A means the middle half of the data is tightly clustered, while a means those values are more spread out. Whenever someone asks, "How much do the typical values vary?" the box gives you a quick visual answer.

The line drawn inside the box marks the median, the center of the dataset. It splits the data so that roughly half the values fall on each side. When you interpret a box plot, you can point to the median line and say, "This is the typical value."

Equally important is where the median line sits within the box. If it falls near the middle of the box, the central 50% of values are fairly balanced around the center. If the median line is closer to $Q_1$ or $Q_3$, the data within that middle 50% are bunched toward one side — a clue about the shape of the distribution that we will explore shortly.

The whiskers extend from the edges of the box out to the minimum and maximum values. They represent the data that falls outside the middle 50%, with each whisker covering roughly 25% of the dataset.

Comparing the two whiskers tells us how the outer portions of the data behave. If both whiskers are about the same length, the data tails are similar on each side. If one whisker is noticeably longer than the other, the data stretches further in that direction — a quick visual clue about whether the distribution is balanced or lopsided.

One of the most practical skills in box plot interpretation is using the relative sizes of the box segments and whiskers to judge the shape of the distribution. There are three features to compare:

1. Left whisker vs. right whisker — Are they roughly equal in length?
2. Left half of the box (from $Q_1$ to the median) vs. right half (from the median to $Q_3$) — Are these two sections about the same width?
3. Overall pattern — Do the shorter segments consistently appear on the same side?

The diagram below illustrates the three common shapes you will encounter:

Let's practice interpreting a box plot from start to finish. Imagine a box plot of weekly grocery bills (in dollars) for a sample of households, displayed along a number line from 30 to 200, with the following five-number summary:

Center: The median is $80, so a typical household spends about $80 per week on groceries.

In this lesson, we moved beyond simply reading values and learned to interpret what a box plot communicates. The box holds the middle 50% of the data and shows how tightly those values cluster. The median line marks the center. And by comparing the lengths of the whiskers and box halves, we can determine whether a distribution is approximately symmetric, skewed left, or skewed right.

Now it is time to put these interpretation skills into action. In the upcoming practice tasks, you will analyze real-world box plots, identify what each feature reveals, and write your own complete interpretations. Let's see how well you can read the story behind the graphic!