A-Level CIE Statistics: Essential Terminology Quick-Reference Guide — A-Level CIE 统计：词汇术语速记指南

📚 A-Level CIE Statistics: Essential Terminology Quick-Reference Guide | A-Level CIE 统计：词汇术语速记指南

1. Introduction: Why Terminology Matters | 引言：为什么术语如此重要

Statistics can feel like learning a new language. Before you can solve problems, analyse data, or interpret results, you must first master the vocabulary that statisticians use. CIE A-Level Statistics (both S1 and S2) is filled with precise terminology — words like “variance,” “binomial,” “hypothesis,” and “significance level” each carry exact mathematical meanings that examiners expect you to know and apply correctly. This guide brings together the essential terms you will encounter across the CIE Statistics syllabus, organised by topic, with clear English definitions and Chinese translations to help you build a solid bilingual foundation.

统计学就像学习一门新语言。在你能解决问题、分析数据或解释结果之前，你必须先掌握统计学家使用的词汇。CIE A-Level 统计（包括 S1 和 S2）充满了精确的术语——像 “variance”、”binomial”、”hypothesis” 和 “significance level” 这样的词汇，每一个都有精确的数学含义，考官期望你正确理解并运用。本指南汇集了 CIE 统计课程中会遇到的所有关键术语，按主题组织，提供清晰的英文定义和中文翻译，帮助你建立扎实的双语基础。

2. Descriptive Statistics: Measures of Central Tendency | 描述性统计：集中趋势的度量

Mean (算术平均数): The arithmetic average of a set of values, calculated by summing all observations and dividing by the number of observations. For a data set x₁, x₂, …, xₙ, the mean x̄ = Σxᵢ / n. The mean is sensitive to outliers and is the most commonly used measure of central tendency. When data is grouped, use the midpoint of each class interval multiplied by the frequency.

算术平均数是一组数值的算术平均值，通过将所有观测值相加再除以观测值个数来计算。对于数据集 x₁, x₂, …, xₙ，平均数 x̄ = Σxᵢ / n。平均数对异常值敏感，是最常用的集中趋势度量。当数据为分组数据时，使用每个组区间的中点乘以频数来计算。

Median (中位数): The middle value when data is arranged in ascending order. If there are n observations, the median is at position (n+1)/2. For grouped data, use linear interpolation: median = L + [(n/2 – F) / f] × w, where L is the lower class boundary, F is the cumulative frequency before the median class, f is the frequency of the median class, and w is the class width. The median is resistant to outliers, making it preferable for skewed distributions.

中位数是数据按升序排列时的中间值。如果有 n 个观测值，中位数位于第 (n+1)/2 的位置。对于分组数据，使用线性插值：中位数 = L + [(n/2 – F) / f] × w，其中 L 是下限，F 是中位数所在组之前的累积频数，f 是中位数所在组的频数，w 是组距。中位数不受异常值影响，因此对于偏态分布更为适用。

Mode (众数): The value that occurs most frequently in a data set. A data set can be unimodal (one mode), bimodal (two modes), or multimodal (more than two modes). For grouped data, the modal class is the class interval with the highest frequency; the mode can be estimated using linear interpolation within that class.

众数是数据集中出现频率最高的值。一个数据集可以是单峰的（一个众数）、双峰的（两个众数）或多峰的（多于两个众数）。对于分组数据，众数所在组是频数最高的组区间；众数可以在该组内使用线性插值来估计。

Quartiles (四分位数): Values that divide an ordered data set into four equal parts. The lower quartile Q₁ is the 25th percentile, the median Q₂ is the 50th percentile, and the upper quartile Q₃ is the 75th percentile. The interquartile range (IQR) = Q₃ – Q₁ measures the spread of the middle 50% of the data and is resistant to outliers.

四分位数是将有序数据集分成四等份的值。下四分位数 Q₁ 是第 25 百分位数，中位数 Q₂ 是第 50 百分位数，上四分位数 Q₃ 是第 75 百分位数。四分位距 (IQR) = Q₃ – Q₁，衡量中间 50% 数据的离散程度，且不受异常值影响。

3. Measures of Dispersion | 离散程度的度量

Range (极差): The simplest measure of spread: range = maximum value − minimum value. While easy to calculate, the range only considers the two extreme values and ignores the distribution of data in between. It is highly sensitive to outliers.

极差是最简单的离散度量：极差 = 最大值 − 最小值。虽然易于计算，但极差只考虑两个极端值，忽略了中间数据的分布情况。它对异常值高度敏感。

Variance (方差): The average of the squared deviations from the mean. For a population of size N: σ² = Σ(xᵢ – μ)² / N. For a sample of size n (the unbiased estimator used in A-Level): s² = Σ(xᵢ – x̄)² / (n-1). A useful computational formula is s² = [Σxᵢ² – (Σxᵢ)²/n] / (n-1). Variance is measured in squared units, which can be difficult to interpret directly.

方差是各数据与平均数之差的平方的平均值。对于大小为 N 的总体：σ² = Σ(xᵢ – μ)² / N。对于大小为 n 的样本（A-Level 中使用的无偏估计量）：s² = Σ(xᵢ – x̄)² / (n-1)。一个有用的计算公式是 s² = [Σxᵢ² – (Σxᵢ)²/n] / (n-1)。方差的单位是原单位的平方，直接解释可能比较困难。

Standard Deviation (标准差): The square root of the variance: s = √s². The standard deviation restores the original units of measurement, making it the most commonly reported measure of dispersion. In a normal distribution, approximately 68% of values lie within 1 standard deviation of the mean, and about 95% within 2 standard deviations. Both S1 and S2 require you to calculate and interpret standard deviations for raw and grouped data.

标准差是方差的平方根：s = √s²。标准差恢复了原始测量单位，使其成为最常报告的离散度量。在正态分布中，大约 68% 的值落在均值 1 个标准差内，约 95% 在 2 个标准差内。S1 和 S2 都要求你计算和解释原始数据和分组数据的标准差。

4. Probability Fundamentals | 概率基础

Experiment (试验): A repeatable process that produces an outcome from a set of possible outcomes. Examples include rolling a die, flipping a coin, or measuring the height of a randomly selected student.

试验是一个可重复的过程，从一组可能的结果中产生一个结果。例如掷骰子、抛硬币或测量随机选择的学生身高。

Sample Space (样本空间): The set of all possible outcomes of an experiment, denoted by S or Ω. For rolling a fair six-sided die, S = {1, 2, 3, 4, 5, 6}. The probability of the entire sample space is always 1: P(S) = 1.

样本空间是试验的所有可能结果的集合，记作 S 或 Ω。对于掷一个公平的六面骰子，S = {1, 2, 3, 4, 5, 6}。整个样本空间的概率始终为 1：P(S) = 1。

Event (事件): A subset of the sample space. For example, when rolling a die, the event “getting an even number” is E = {2, 4, 6}. The probability of an event E is P(E) = n(E) / n(S) for equally likely outcomes.

事件是样本空间的子集。例如，掷骰子时，事件”得到偶数”是 E = {2, 4, 6}。对于等可能结果，事件 E 的概率为 P(E) = n(E) / n(S)。

Mutually Exclusive Events (互斥事件): Two events A and B are mutually exclusive if they cannot occur simultaneously: P(A ∩ B) = 0. For mutually exclusive events, P(A ∪ B) = P(A) + P(B). For example, when drawing one card from a deck, “drawing a heart” and “drawing a spade” are mutually exclusive.

如果两个事件 A 和 B 不能同时发生，则它们是互斥的：P(A ∩ B) = 0。对于互斥事件，P(A ∪ B) = P(A) + P(B)。例如，从一副牌中抽一张牌，”抽到红心”和”抽到黑桃”是互斥的。

Independent Events (独立事件): Two events A and B are independent if the occurrence of one does not affect the probability of the other: P(A ∩ B) = P(A) × P(B), or equivalently, P(A|B) = P(A). Independence is a crucial concept in probability — do not confuse it with mutual exclusivity. Rolling two dice produces independent outcomes.

如果事件 A 的发生不影响事件 B 的概率，则它们是独立的：P(A ∩ B) = P(A) × P(B)，或等价地 P(A|B) = P(A)。独立性是概率中的一个关键概念——不要与互斥性混淆。掷两个骰子产生独立的结果。

Conditional Probability (条件概率): The probability of event A occurring given that event B has already occurred, denoted P(A|B). The formula is P(A|B) = P(A ∩ B) / P(B), provided P(B) > 0. Conditional probability is fundamental to Bayes’ theorem and is tested extensively in both S1 and S2.

条件概率是已知事件 B 已发生的情况下事件 A 发生的概率，记作 P(A|B)。公式为 P(A|B) = P(A ∩ B) / P(B)，前提是 P(B) > 0。条件概率是贝叶斯定理的基础，在 S1 和 S2 中都有广泛考查。

5. Permutations and Combinations | 排列与组合

Factorial (阶乘): n factorial, denoted n!, is the product of all positive integers from 1 to n: n! = n × (n-1) × (n-2) × … × 3 × 2 × 1. By convention, 0! = 1. Factorials count the number of ways to arrange n distinct objects in a line.

n 的阶乘，记作 n!，是从 1 到 n 的所有正整数的乘积：n! = n × (n-1) × (n-2) × … × 3 × 2 × 1。按照惯例，0! = 1。阶乘计算将 n 个不同物体排成一行的方法数。

Permutation (排列): An ordered arrangement of a subset of items. The number of permutations of r objects chosen from n distinct objects is ⁿPᵣ = n! / (n-r)!. Order matters in permutations. For example, the number of ways to award gold, silver, and bronze medals to 8 runners is ⁸P₃ = 8! / 5! = 336.

排列是部分元素的有序排列。从 n 个不同物体中选取 r 个的排列数为 ⁿPᵣ = n! / (n-r)!。排列中顺序很重要。例如，将金、银、铜牌授予 8 名跑步者的方法数为 ⁸P₃ = 8! / 5! = 336。

Combination (组合): A selection of items where order does not matter. The number of combinations of r objects chosen from n distinct objects is ⁿCᵣ = n! / [r!(n-r)!], also written as the binomial coefficient (n choose r). For example, the number of ways to choose a 3-person committee from 10 people is ¹⁰C₃ = 120.

组合是顺序无关的选择。从 n 个不同物体中选取 r 个的组合数为 ⁿCᵣ = n! / [r!(n-r)!]，也写作二项式系数。例如，从 10 人中选出一个 3 人委员会的方法数为 ¹⁰C₃ = 120。

6. Discrete Random Variables | 离散随机变量

Random Variable (随机变量): A variable whose value is determined by the outcome of a random experiment. A discrete random variable can only take specific, countable values (often integers). Denoted by uppercase letters like X, with lowercase letters like x representing specific values that X can take.

随机变量是其值由随机试验结果决定的变量。离散随机变量只能取特定的可数值（通常是整数）。用大写字母如 X 表示，小写字母如 x 表示 X 可以取的具体值。

Probability Distribution (概率分布): A listing of all possible values a discrete random variable can take, along with their corresponding probabilities. For a valid probability distribution: ΣP(X = x) = 1 and 0 ≤ P(X = x) ≤ 1 for all x. The distribution is often presented in a table.

概率分布列出离散随机变量所有可能的取值及其对应的概率。对于一个有效的概率分布：ΣP(X = x) = 1，且对所有 x 有 0 ≤ P(X = x) ≤ 1。分布通常以表格形式呈现。

Expected Value (期望值): The long-run average of a random variable, denoted E(X) or μ. For a discrete random variable, E(X) = Σ[x × P(X = x)]. The expected value is a measure of central tendency for a probability distribution. Key properties: E(aX + b) = aE(X) + b and E(aX + bY) = aE(X) + bE(Y), where a and b are constants and X and Y are random variables.

期望值是随机变量的长期平均值，记作 E(X) 或 μ。对于离散随机变量，E(X) = Σ[x × P(X = x)]。期望值是概率分布的集中趋势度量。关键性质：E(aX + b) = aE(X) + b，且 E(aX + bY) = aE(X) + bE(Y)，其中 a 和 b 是常数，X 和 Y 是随机变量。

Variance of a Random Variable (随机变量的方差): Var(X) = E[(X – μ)²] = E(X²) – [E(X)]². The variance measures the expected squared deviation from the mean. Key properties: Var(aX + b) = a²Var(X) and, for independent random variables X and Y, Var(aX + bY) = a²Var(X) + b²Var(Y).

Var(X) = E[(X – μ)²] = E(X²) – [E(X)]²。方差衡量偏离均值的期望平方差。关键性质：Var(aX + b) = a²Var(X)，且对于独立的随机变量 X 和 Y，Var(aX + bY) = a²Var(X) + b²Var(Y)。

7. The Binomial Distribution | 二项分布

Binomial Distribution (二项分布): A discrete probability distribution, denoted X ~ B(n, p), that models the number of successes in n independent trials, where each trial has the same probability of success p. The conditions for a binomial distribution are: (1) fixed number of trials n, (2) each trial is independent, (3) only two possible outcomes per trial (success or failure), (4) constant probability of success p for each trial.

二项分布是一种离散概率分布，记作 X ~ B(n, p)，用于建模 n 次独立试验中成功的次数，其中每次试验有相同的成功概率 p。二项分布的条件是：(1) 固定次数的试验 n，(2) 每次试验相互独立，(3) 每次试验只有两种可能的结果（成功或失败），(4) 每次试验的成功概率 p 恒定。

Binomial Probability Formula (二项概率公式): P(X = r) = ⁿCᵣ × pʳ × (1-p)ⁿ⁻ʳ = ⁿCᵣ × pʳ × qⁿ⁻ʳ, where q = 1 – p. This formula gives the probability of exactly r successes in n trials. On the CIE exam, you may need to calculate cumulative probabilities such as P(X ≤ r) or P(X ≥ r), which can be found using statistical tables or by summing individual probabilities.

P(X = r) = ⁿCᵣ × pʳ × (1-p)ⁿ⁻ʳ = ⁿCᵣ × pʳ × qⁿ⁻ʳ，其中 q = 1 – p。此公式给出 n 次试验中恰好 r 次成功的概率。在 CIE 考试中，你可能需要计算累积概率，如 P(X ≤ r) 或 P(X ≥ r)，可以使用统计表或通过对单个概率求和来求得。

Mean and Variance of Binomial Distribution (二项分布的均值和方差): If X ~ B(n, p), then the expected value E(X) = np and the variance Var(X) = npq = np(1-p). These formulas are derived from the properties of expectation and variance and are essential for solving binomial problems efficiently — especially in hypothesis testing (S2).

如果 X ~ B(n, p)，则期望值 E(X) = np，方差 Var(X) = npq = np(1-p)。这些公式由期望和方差的性质导出，对于高效解决二项分布问题至关重要——尤其是在假设检验 (S2) 中。

8. The Normal Distribution | 正态分布

Normal Distribution (正态分布): A continuous probability distribution, denoted X ~ N(μ, σ²), with a bell-shaped curve that is symmetric about the mean μ. The total area under the curve equals 1. Approximately 68% of values fall within μ ± σ, 95% within μ ± 2σ, and 99.7% within μ ± 3σ (the empirical rule). The normal distribution is the most important distribution in statistics and forms the basis for much of the S2 syllabus.

正态分布是一种连续概率分布，记作 X ~ N(μ, σ²)，具有关于均值 μ 对称的钟形曲线。曲线下的总面积为 1。大约 68% 的值落在 μ ± σ 内，95% 在 μ ± 2σ 内，99.7% 在 μ ± 3σ 内（经验法则）。正态分布是统计学中最重要的分布，是 S2 课程大部分内容的基础。

Standard Normal Distribution (标准正态分布): The special case of the normal distribution with mean 0 and variance 1, denoted Z ~ N(0, 1). Any normal random variable X ~ N(μ, σ²) can be standardised using the z-score transformation: Z = (X – μ) / σ. Standard normal distribution tables provide Φ(z) = P(Z < z), allowing probability calculations for any normal distribution.

标准正态分布是均值为 0、方差为 1 的正态分布特例，记作 Z ~ N(0, 1)。任何正态随机变量 X ~ N(μ, σ²) 都可以通过 z 分数变换标准化：Z = (X – μ) / σ。标准正态分布表提供 Φ(z) = P(Z < z)，使得任何正态分布的概率计算成为可能。

Continuity Correction (连续性校正): When approximating a binomial distribution B(n, p) with a normal distribution N(np, npq), a continuity correction must be applied because the binomial is discrete while the normal is continuous. To approximate P(X ≤ r), use P(X < r + 0.5) in the normal. To approximate P(X ≥ r), use P(X > r – 0.5). The approximation is valid when np > 5 and nq > 5.

当用正态分布 N(np, npq) 近似二项分布 B(n, p) 时，必须应用连续性校正，因为二项分布是离散的而正态分布是连续的。要近似 P(X ≤ r)，在正态中使用 P(X < r + 0.5)。要近似 P(X ≥ r)，使用 P(X > r – 0.5)。当 np > 5 且 nq > 5 时，该近似有效。

9. Sampling and Estimation | 抽样与估计

Population (总体): The entire set of individuals or items that are of interest in a statistical investigation. The population mean is denoted μ and the population variance is denoted σ². In practice, we rarely have access to an entire population, which is why sampling is necessary.

总体是统计调查中所有感兴趣的个体或项目的集合。总体均值记作 μ，总体方差记作 σ²。在实践中，我们很少能够接触整个总体，这就是抽样必要的原因。

Sample (样本): A subset of the population selected for study. The sample mean x̄ and sample variance s² are statistics calculated from the sample. A sample must be representative of the population to allow valid inference — random sampling is the gold standard.

样本是从总体中选择用于研究的子集。样本均值 x̄ 和样本方差 s² 是从样本计算出的统计量。样本必须能代表总体才能进行有效推断——随机抽样是黄金标准。

Sampling Distribution (抽样分布): The probability distribution of a sample statistic (such as the sample mean) over all possible samples of a given size from the population. The Central Limit Theorem states that for large n, the sampling distribution of the sample mean is approximately normal with mean μ and variance σ²/n, regardless of the shape of the population distribution.

抽样分布是样本统计量（如样本均值）在来自总体的给定大小的所有可能样本上的概率分布。中心极限定理指出，对于大 n，样本均值的抽样分布近似服从均值为 μ、方差为 σ²/n 的正态分布，无论总体分布的形状如何。

Unbiased Estimator (无偏估计量): An estimator whose expected value equals the true population parameter. The sample mean x̄ is an unbiased estimator of μ since E(X̄) = μ. The sample variance s² = Σ(xᵢ – x̄)² / (n-1) is an unbiased estimator of σ² — this is why we divide by (n-1) rather than n. The CIE syllabus explicitly tests your understanding of why the (n-1) denominator is used.

无偏估计量是其期望值等于真实总体参数的估计量。样本均值 x̄ 是 μ 的无偏估计量，因为 E(X̄) = μ。样本方差 s² = Σ(xᵢ – x̄)² / (n-1) 是 σ² 的无偏估计量——这就是为什么我们除以 (n-1) 而非 n。CIE 课程明确考查你对为什么使用 (n-1) 分母的理解。

10. Hypothesis Testing | 假设检验

Null Hypothesis (原假设): Denoted H₀, this is the default position that there is no effect or no difference. In CIE statistics, H₀ typically states that a population parameter (mean, proportion, or binomial parameter p) equals a specific value. Hypothesis testing is about assessing whether the evidence against H₀ is strong enough to reject it.

原假设记作 H₀，是没有效应或没有差异的默认立场。在 CIE 统计中，H₀ 通常陈述某个总体参数（均值、比例或二项参数 p）等于某个特定值。假设检验是关于评估反对 H₀ 的证据是否足够强以拒绝它。

Alternative Hypothesis (备择假设): Denoted H₁ or Hₐ, this is the statement that we accept if H₀ is rejected. It can be one-tailed (e.g., p > 0.5 or p < 0.5) or two-tailed (e.g., p ≠ 0.5). The choice between one-tailed and two-tailed tests depends on the research question and must be clearly stated before analysing the data.

备择假设记作 H₁ 或 Hₐ，是如果 H₀ 被拒绝时我们接受的陈述。它可以是单尾的（如 p > 0.5 或 p < 0.5）或双尾的（如 p ≠ 0.5）。单尾和双尾检验的选择取决于研究问题，必须在分析数据之前明确说明。

Significance Level (显著性水平): Denoted α, this is the probability of making a Type I error — rejecting H₀ when it is actually true. Common values are 5% (α = 0.05) and 1% (α = 0.01). The significance level defines the critical region: if the test statistic falls in this region, we reject H₀. A smaller α means a more stringent test.

显著性水平记作 α，是犯第一类错误的概率——当 H₀ 实际为真时拒绝它。常用值为 5%（α = 0.05）和 1%（α = 0.01）。显著性水平定义了临界区域：如果检验统计量落在此区域内，我们就拒绝 H₀。较小的 α 意味着更严格的检验。

p-value (p 值): The probability of observing a test statistic at least as extreme as the one obtained, assuming H₀ is true. If p-value < α, we reject H₀. If p-value > α, we do not reject H₀. The p-value approach provides more information than simply comparing a test statistic to a critical value, and is increasingly expected in CIE S2 answers.

p 值是在 H₀ 为真的假设下，观察到至少与所得统计量一样极端的检验统计量的概率。如果 p 值 < α，我们拒绝 H₀。如果 p 值 > α，我们不拒绝 H₀。p 值方法比简单地将检验统计量与临界值比较提供更多信息，在 CIE S2 答案中越来越被期望。

Type I and Type II Errors (第一类和第二类错误): A Type I error occurs when H₀ is true but we reject it (probability = α). A Type II error occurs when H₀ is false but we fail to reject it (probability = β). The power of a test is 1 – β, the probability of correctly rejecting a false null hypothesis. Understanding these errors is essential for evaluating the reliability of statistical conclusions.

第一类错误发生在 H₀ 为真但我们拒绝了它（概率 = α）。第二类错误发生在 H₀ 为假但我们未能拒绝它（概率 = β）。检验的功效是 1 – β，即正确拒绝错误原假设的概率。理解这些错误对于评估统计结论的可靠性至关重要。

11. Correlation and Regression | 相关与回归

Correlation (相关性): A measure of the strength and direction of a linear relationship between two variables. In CIE Statistics, we focus on the Product Moment Correlation Coefficient (PMCC), denoted r for sample data. The PMCC ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation), with 0 indicating no linear correlation. Remember: correlation does not imply causation.

相关性是衡量两个变量之间线性关系的强度和方向的度量。在 CIE 统计中，我们关注积矩相关系数 (PMCC)，样本数据记作 r。PMCC 的取值范围从 -1（完全负相关）到 +1（完全正相关），0 表示没有线性相关。请记住：相关并不意味着因果。

Regression Line (回归线): The line of best fit for a set of bivariate data. The least squares regression line of y on x has equation y = a + bx, where b = Sxy / Sxx and a = ȳ – bx̄. Here, Sxy = Σ(xᵢ – x̄)(yᵢ – ȳ) = Σxᵢyᵢ – (Σxᵢ)(Σyᵢ)/n and Sxx = Σ(xᵢ – x̄)² = Σxᵢ² – (Σxᵢ)²/n. The regression line minimises the sum of squared vertical deviations from the line.

回归线是双变量数据的最佳拟合线。y 对 x 的最小二乘回归线方程为 y = a + bx，其中 b = Sxy / Sxx，a = ȳ – bx̄。这里，Sxy = Σ(xᵢ – x̄)(yᵢ – ȳ) = Σxᵢyᵢ – (Σxᵢ)(Σyᵢ)/n，Sxx = Σ(xᵢ – x̄)² = Σxᵢ² – (Σxᵢ)²/n。回归线最小化到线的垂直偏差的平方和。

Interpolation vs Extrapolation (内插与外推): Interpolation is predicting a y-value for an x-value within the range of the observed data — this is generally reliable. Extrapolation is predicting beyond the observed range — this is unreliable because the linear relationship may not hold outside the data range. CIE exam questions frequently ask you to comment on the reliability of predictions, and knowing this distinction is crucial.

内插是在观测数据范围内预测 x 值对应的 y 值——这通常是可靠的。外推是超出观测范围进行预测——这不可靠，因为线性关系可能在数据范围之外不成立。CIE 考题经常要求你评论预测的可靠性，了解这一区别至关重要。

12. Summary and Exam Tips | 总结与考试技巧

Mastering CIE A-Level Statistics requires more than just memorising formulas — you need to understand the precise meaning of each term and when to use it. Here are key exam tips: (1) Always state your hypotheses clearly before conducting a test; write H₀ and H₁ in words and symbols. (2) For normal distribution problems, draw a diagram and shade the relevant area; it reduces careless errors. (3) Show your working step by step — CIE awards method marks even if your final answer is wrong. (4) For binomial probability questions, check whether the question asks for “exactly,” “at most,” or “at least” — this changes how you use the tables. (5) When using a normal approximation to the binomial, always state and apply the continuity correction. (6) Write conclusions in context after a hypothesis test — “reject H₀” alone is not enough; state what this means in the given scenario. (7) Know your calculator functions for binomial and normal probabilities, but also be prepared to use the statistical tables — the exam may require either approach.

掌握 CIE A-Level 统计需要的不仅仅是记忆公式——你需要理解每个术语的精确含义以及何时使用它。以下是关键的考试技巧：(1) 在进行检验之前始终清楚地陈述你的假设；用文字和符号写出 H₀ 和 H₁。(2) 对于正态分布问题，画出图表并标出相关区域；这能减少粗心错误。(3) 逐步展示你的解题过程——即使最终答案错误，CIE 也会给予方法分。(4) 对于二项概率问题，检查题目要求的是”恰好”、”至多”还是”至少”——这会影响你如何使用统计表。(5) 当用正态分布近似二项分布时，始终陈述并应用连续性校正。(6) 假设检验后在上下文中撰写结论——仅仅说”拒绝 H₀”是不够的；需要说明这在给定情境中意味着什么。(7) 了解你的计算器如何计算二项和正态概率，但也要准备好使用统计表——考试可能要求使用其中任何一种方法。

This terminology guide covers the essential vocabulary from both S1 and S2 of the CIE A-Level Statistics syllabus. Keep it handy for revision, and practise using these terms accurately in your written answers — precise language is what distinguishes top-scoring candidates from the rest. Good luck!

本术语指南涵盖了 CIE A-Level 统计课程（S1 和 S2）的核心词汇。复习时请随时参考，并练习在书面答案中准确使用这些术语——精确的语言是高分考生与其他考生的区别所在。祝你好运！