AP Statistics: 250 Key Terms, Conditions and Traps
Most lost points come from checking the wrong condition, not from forgetting a formula.
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| 表 | 裏 |
|---|---|
| Categorical variable — what is it? | A variable that places each individual into one of several groups. A variable stored as a number can still be categorical, such as a zip code or a jersey number. |
| Distribution — what is it? | The values a variable takes and how often it takes them. A description that names only the centre is incomplete and loses credit. |
| Dotplot — what is it? | A display that shows every observation as a dot above its value on a number line. It becomes unreadable for large data sets. A histogram is the better choice. |
| Mean — what is it? | The arithmetic average, found by summing the values and dividing by the number of values. It is not resistant: a single extreme value shifts it noticeably. |
| Range — what is it? | The difference between the maximum and the minimum. Reporting the range as an interval such as from 3 to 19 does not answer the question asked. |
| Outlier — what is it? | An observation that falls well outside the overall pattern of the data. An outlier is not automatically an error and should not be deleted without a reason. |
| Percentile — what is it? | The percentage of observations that fall at or below a given value. A percentile is a position, not a score. The 90th percentile is not a score of 90. |
| Effect of adding a constant — what is it? | What happens to a distribution when the same number is added to every value. Adding a constant does not change the shape or the standard deviation. |
| Density curve — what is it? | A smooth curve that describes the overall pattern of a distribution. Areas give proportions. The height of the curve is not a probability. |
| Normal distribution — what is it? | A symmetric, bell-shaped density curve described by its mean and standard deviation. Bell-shaped is not the same as normal. Check the context before assuming normality. |
| Comparing distributions — what is it? | Describing two or more distributions relative to each other. Describing each distribution separately is not a comparison and loses credit. |
| Explanatory variable — what is it? | The variable that may help explain or predict changes in another variable. Calling it the explanatory variable does not establish that it causes anything. |
| Correlation — what is it? | A number measuring the strength and direction of a linear relationship. It measures only linear association. A strong curved relationship can give a correlation near zero. |
| Residual — what is it? | The difference between an observed response and the value predicted by the line. Reversing the order of the subtraction reverses every sign. |
| Influential point — what is it? | An observation that substantially changes the regression line if it is removed. An outlier in the response direction may have a large residual yet little influence. |
| Two-way table — what is it? | A table showing counts for the combinations of two categorical variables. Comparing raw counts across rows of different sizes leads to a wrong conclusion. |
| Population — what is it? | The entire group of individuals about which information is wanted. It is the group of interest, not the group that happens to be available. |
| Simple random sample — what is it? | A sample chosen so that every group of the given size has an equal chance of being selected. Giving every individual an equal chance is not enough. Every possible group must be equally likely. |
| Bias — what is it? | A systematic tendency for a study to favour certain outcomes. Bias is a property of the method, not of any single sample result. |
| Observational study — what is it? | A study that records data without attempting to influence the responses. Adjusting for known variables does not turn an observational study into an experiment. |
| Statistically significant result — what is it? | A difference too large to be explained plausibly by chance alone. Significant does not mean large or important in a practical sense. |
| Conditional probability — what is it? | The probability of an event given that another event has occurred. The order of the two events matters. The two conditional probabilities are generally different. |
| Simulation — what is it? | Imitating a chance process using random digits or a random device. A description must state how digits are assigned, what one trial is, and what is recorded. |
| Standard error — what is it? | An estimate of the standard deviation of a statistic, computed from sample data. It estimates variability in the statistic, not variability in the data. |
| Sampling distribution of a difference of proportions — what is it? | The distribution of the difference between two independent sample proportions. Variances add even though the means subtract. |
| Independence condition — what is it? | The requirement that observations do not influence each other. It also requires the two samples to be independent when groups are compared. |
| t distribution — what is it? | A family of distributions used when the population standard deviation is unknown. Its shape depends on the degrees of freedom, so a single table row is not enough. |
| Type I error — what is it? | Rejecting a null hypothesis that is actually true. It is the error of finding an effect that is not there. |
| Chi-square goodness of fit test — what is it? | A test comparing observed counts in one categorical variable with a claimed distribution. It uses counts, not percentages. Converting to percentages first invalidates the test. |
| Inference for the slope — what is it? | Testing or estimating the slope of a population regression line. Rejecting the null hypothesis of zero slope does not establish causation. |
このデッキについて
A student who can state the central limit theorem still loses the point if they apply it to the data instead of to the sample mean. The same is true of the two standard errors for a proportion: the interval uses the sample value and the test uses the hypothesised one, and using the wrong one quietly changes the answer. Statistics is not short on definitions. It is short on students who can say which condition a procedure needs and why. This deck is 250 cards, one term per card, with the back giving the definition and, where it applies, a line on the confusion that costs the point. The sections follow the shape of the course: exploring data, relationships, collecting data, probability, sampling distributions, and inference. No worked arithmetic appears anywhere. Procedures are described in words, because on the free-response section the credit sits in naming the procedure, checking its conditions and stating the conclusion in context, and a calculator handles the rest. Once the deck is on a spaced-repetition schedule, the terms you can already place stop coming back and the pairs you keep reversing return until they stop being a coin flip.
よくある質問
- What is in each section of the deck?
- Exploring data has 45 cards, relationships 40, collecting data 40, probability 45, sampling distributions 35, and inference 45, for 250 in total. Every card carries section and subtopic tags, so you can drill only the sampling distributions, only the study designs, or only the chi-square procedures.
- Does it help with the free-response section?
- It covers the parts of a free-response answer that are pure recall: which procedure applies, which conditions it needs, and how a conclusion must be worded in context. It does not replace writing full answers, because the marks for organisation and communication only come from practice on paper.
- Is a calculator needed alongside the deck?
- Not for the cards themselves, which contain no arithmetic. You will still need one for actual problems, and knowing which calculator function corresponds to which procedure is worth practising separately. The deck tells you which procedure to reach for and what has to be checked first.
- Can I import the whole deck on the free plan?
- Yes. Importing a saved deck runs no new AI generation and does not use your AI allowance, so the free plan imports all 250 cards. You can study, edit and delete them afterwards.
- Will importing it twice create duplicates?
- No. Cards you already have are skipped and only cards added in a revision come through. Including re-imports after deleting it, one official deck can be imported three times per account.
- Can I use it on the web and in the mobile app?
- Yes. The deck is added to your account rather than to a device, so the same cards and the same progress are there on the web, on iOS and on Android.
- Can I edit the cards after importing?
- Yes. Imported cards are yours: you can edit both sides, delete cards you do not need, change tags, and move cards to another deck.
No official exam questions are reproduced. Every card was written for this deck.Advanced Placement is a trademark of College Board. This deck is not produced, endorsed or approved by College Board.Editorial reference date 2026-08-31.