Because the mean is the sum divided by the count, the sum is the mean times the count, and that turns every mean question into an addition problem. If six numbers have mean 15, their sum is 90; if a seventh is added and the mean becomes 17, the new sum is 119, so the seventh number is 29. If a class of 20 has mean 78 and one score is removed leaving a mean of 80 for the remaining 19, the removed score is 1,560 - 1,520 = 40. Two groups combine by total, not by averaging the means: 25 boys with mean 70 and 15 girls with mean 78 have a combined total of 1,750 + 1,170 = 2,920 over 40 students, a mean of 73, which is a weighted mean pulled toward the larger group. Only when the groups are equal in size does the combined mean equal the average of the means. The same reasoning answers questions about how a mean changes when every value is increased by a constant (the mean rises by that constant) or multiplied by one (the mean is multiplied).
The median depends only on order and the mean on every value, and the difference between them is a statement about shape. In a distribution skewed to the right, with a tail of unusually large values, the mean is greater than the median; skewed to the left, the mean is less. Removing an outlier moves the mean substantially and the median little; the range collapses; the standard deviation falls, because standard deviation measures spread around the mean and the outlier was the farthest point. Comparing two distributions, the one with values more concentrated around its center has the smaller standard deviation regardless of where that center is, and two distributions with identical shapes shifted along the axis have the same standard deviation and different means. A question that says "which measure is most affected by removing the value 100" from a set of otherwise small values is asking you to name the mean, or the range, not the median.
A table of data with equally spaced inputs is linear if the outputs change by a constant difference and exponential if they change by a constant ratio. Outputs 300, 360, 432 at inputs 0, 1, 2 have differences 60 and 72, not constant, but ratios 1.2 and 1.2, so the model is y = 300(1.2)^x, a 20 percent increase per step. Percent problems with an unknown original amount are solved the same way in reverse: if a price rose 20 percent and then fell 25 percent to end at 72 dollars, the original was 72 / (1.20 x 0.75) = 80. A chain of percent changes is one multiplier, and the original is the final value divided by it.
Conditional probability with percents is a fraction of a fraction. If 40 percent of a group own a dog and 25 percent of the group own both a dog and a cat, then among dog owners the fraction owning a cat is 0.25 / 0.40 = 0.625. Formally, the probability of A given B is the probability of both divided by the probability of B. Two events are independent when knowing one does not change the other: the probability of A given B equals the probability of A. In a two-way table, independence shows up as every row having the same proportions; a question may ask whether the data suggest the two variables are associated, and the answer is yes when the conditional proportions differ across rows.
Statistical conclusions are limited by how the data were collected. Random sampling from a population is what allows a sample result to be generalized to that population: a random sample of a city's adults tells you about that city's adults, and a survey of people at a gym tells you about gym-goers only. Random assignment of participants to treatments is what allows a cause-and-effect conclusion, because it makes the treatment groups alike in everything but the treatment. An observational study, where people chose their own group, can show an association but not causation, since the groups may differ in other ways. So a study that randomly samples a school's students and randomly assigns them to two teaching methods can conclude that the method caused the difference for students at that school; the same study without random assignment can conclude only that the method and the outcome are associated; and with neither, only that they were associated among the participants.
A sample statistic estimates a population parameter with a margin of error, and the true value is plausibly anywhere within the margin on either side of the estimate. The margin depends on the sample size, shrinking in proportion to the square root of the size: a sample four times as large has half the margin, a sample nine times as large has a third. It does not depend on the size of the population. The margin says nothing about the individuals sampled, whose values are known exactly, and it does not guarantee that the true value lies in the interval; it identifies the range of values consistent with the sample. Answer choices that say the sample's own value lies in the interval, that the population value is certainly in it, or that a smaller sample would be more precise are the standard wrong answers.
Rates with compound units need every unit converted, and squared or cubed units convert by the square or cube of the factor. One meter is 100 centimeters, so one square meter is 100² = 10,000 square centimeters and one cubic meter is 1,000,000 cubic centimeters. Density is mass per unit volume: an object of volume 4,000 cubic centimeters and density 2.5 grams per cubic centimeter has mass 10,000 grams, which is 10 kilograms. Write every quantity with its units and cancel; if the leftover unit is not the one the question asks for, the conversion is incomplete.