Year 13 Edexcel Statistics: Quick Vocabulary Mnemonic Guide | 爱德思Year 13统计：词汇术语速记指南

📚 Year 13 Edexcel Statistics: Quick Vocabulary Mnemonic Guide | 爱德思Year 13统计：词汇术语速记指南

Mastering the vocabulary of Year 13 Edexcel Statistics is essential for interpreting questions accurately and scoring top marks. This guide provides clear explanations, paired examples, and memorable mnemonics for all key terms you will encounter in the A2 statistics syllabus, including hypothesis testing, distributions, and sampling.

掌握Year 13爱德思统计学科的术语是准确理解题目并取得高分的关键。本指南将为你清晰讲解A2统计大纲中的所有核心术语，包括假设检验、概率分布和抽样方法，并提供中英配对释义和实用记忆法。

1. Key Concepts in Hypothesis Testing | 假设检验核心术语

The null hypothesis (H₀) is a statement of no effect or no difference, assumed true until evidence suggests otherwise. The alternative hypothesis (H₁ or Hₐ) is what you want to prove, often indicating a change, difference, or association.

原假设 (H₀) 表述为“无效应”或“无差异”，在被证据否定之前我们都假定它为真。备择假设 (H₁ 或 Hₐ) 则是研究者希望证实的命题，通常表明存在变化、差异或关联。

The significance level (α) is the probability of rejecting H₀ when it is actually true (Type I error). The p-value is the probability of obtaining a test statistic at least as extreme as the observed one, assuming H₀ is true. If p-value ≤ α, we reject H₀.

显著性水平 (α) 是当原假设为真时拒绝它的概率（第一类错误）。p值是在原假设成立的条件下，得到当前及更极端检验统计量的概率。若 p ≤ α，则拒绝原假设。

A one-tailed test examines whether a parameter is greater than or less than a specified value, while a two-tailed test checks for any difference (either direction). The critical region is chosen accordingly, with the significance level split for two tails.

单尾检验考察参数是否大于或小于某一特定值；双尾检验则检验是否存在任何方向上的差异。临界域相应划分，双尾检验将α平分至左右两侧。

2. Probability Distributions Overview | 概率分布总览

A probability distribution describes how probabilities are allocated across all possible outcomes of a random variable. For discrete variables, we use a probability mass function (PMF) giving P(X=x). For continuous variables, a probability density function (PDF) f(x) is used, where probabilities are found as areas under the curve.

概率分布描述随机变量所有可能结果的概率分配方式。离散随机变量使用概率质量函数(PMF)表示 P(X=x)；连续随机变量则用概率密度函数(PDF) f(x) 刻画，概率对应曲线下面积。

The cumulative distribution function (CDF) F(x)=P(X ≤ x) gives the accumulated probability up to x. The expectation E(X) is the long-run average, and variance Var(X) measures spread.

累积分布函数(CDF) F(x)=P(X ≤ x) 给出到x为止的累计概率；期望 E(X) 是长期平均，方差 Var(X) 衡量离散程度。

3. Poisson Distribution in Detail | 泊松分布详解

A Poisson distribution models the number of independent, random events occurring in a fixed interval of time or space, given a known average rate λ (lambda). Conditions: events occur singly, at a constant average rate, and independently of each other.

泊松分布用于模拟固定时间或空间间隔内独立随机事件的发生次数，已知平均发生率 λ（lambda）。条件：事件单独发生，平均速率恒定，且相互独立。

The probability mass function is:

P(X = x) = (e⁻λ λˣ) / x! for x = 0, 1, 2, …

概率分布为：P(X = x) = (e⁻λ λˣ) / x! ，x取非负整数。

Important properties: The mean of a Poisson is λ and the variance is also λ. If X~Po(λ₁) and Y~Po(λ₂) are independent, then X+Y~Po(λ₁+λ₂). This additivity is unique to the Poisson distribution.

重要性质：均值为 λ，方差也为 λ。若独立的 X~Po(λ₁) 和 Y~Po(λ₂)，则 X+Y~Po(λ₁+λ₂)。这种可加性是泊松分布的一个特色。

4. Continuous Distributions: Normal & Others | 连续分布：正态及其他

The normal distribution N(μ, σ²) is symmetric and bell-shaped, described by its mean μ and standard deviation σ. About 68% of data lie within μ ± σ, 95% within μ ± 2σ, and 99.7% within μ ± 3σ.

正态分布 N(μ, σ²) 呈对称钟形，由均值 μ 和标准差 σ 决定。约68%的数据落在 μ ± σ 内，95%在 μ ± 2σ 内，99.7%在 μ ± 3σ 内。

To standardise, we use Z = (X – μ) / σ, giving Z~N(0,1). Probability calculations use the standard normal table or inverse normal functions.

标准化公式为 Z = (X – μ) / σ，所得 Z~N(0,1)。概率计算借助标准正态分布表或逆正态函数。

Other continuous distributions that may appear include the continuous uniform distribution where probabilities are proportional to length, and Student’s t-distribution for small samples when σ is unknown.

其他可能出现的连续分布包括：连续均匀分布（概率与区间长度成正比）和用于小样本且总体标准差未知时的学生 t 分布。

5. Sampling Methods & Bias | 抽样方法与偏差

A simple random sample gives every member of the population an equal chance of being selected. Stratified sampling divides the population into distinct

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