A nutrition label is math you already do without calling it math: servings per container, percent of a daily value, grams per 100. Problem-Solving and Data Analysis is that same arithmetic on a bigger stage — ratios, rates, percentages, probability, and reading the story a table or graph is telling.
This is the one math domain where the hard part is often reading, not calculating. A figure holds more than one number, and the question asks you to combine two of them.
The method
- Track units the whole way. Carry “miles,” “dollars,” “per hour” through every step. Units catch more errors on this section than any other check — a stray “per” flags a flipped ratio.
- Set up proportions for rates. “3 machines fill 120 boxes in 4 hours” is a proportion waiting to be written, not a puzzle to reason through.
- Read the axes and headers first. Before touching a number in a chart, read what each axis measures and in what units. Half of data-question errors happen on the label, not the math.
What turns them hard
Easy items read one value straight off a chart. Hard items demand a second read that combines two. A two-way table asks for a conditional probability — the fraction within one row, not out of the whole — so the denominator is a row total, not the grand total. A bar chart asks for the percent change from one year to the next, which needs both bars and a subtraction before the division. A statistics item asks which of two data sets is more spread out, not which has the higher average — center and spread are different questions, and the test counts on you answering the one you weren’t asked.
Slow down at the figure, speed up at the arithmetic. The trap is almost never a hard calculation; it is a fast misread of what the table was counting.
Common questions
What does Problem-Solving and Data Analysis test?
Ratios, rates, proportions, percentages, unit conversion, probability, basic statistics (mean, median, spread), and interpreting data in tables and graphs.
What is the most common data-analysis trap?
Using the wrong denominator on a conditional probability — dividing by the grand total instead of the relevant row or column total — and confusing a data set’s center with its spread.