Probability Calculator|概率计算器

概率计算器 | Probability Calculator

概率计算器 Probability Calculator

用于基础概率学习:单事件概率、补事件概率、独立事件的交集/并集、互斥事件并集等。
For basic probability learning: single-event probability, complement, independent events (intersection/union), and mutually exclusive union.

1. 输入参数 · Inputs
你可以从“计数法(有利/总数)”开始入门;若已知 P(A)、P(B),可直接选择独立/互斥场景进行组合计算。
Start with counting (favorable/total). If you already know P(A), P(B), choose independent/mutually exclusive scenarios.
掷骰子点数为 1:k=1, n=6 → P(A)=1/6
计数法(等可能) · Counting (equally likely):
当每个结果“同等可能”时,概率可写为:P(A)=有利结果数/总结果数。
If outcomes are equally likely, probability is: P(A)=favorable/total.
3. 使用说明 · Notes
  • 工具定位 · What this tool is for:
    这是一个“入门级概率计算器”,专门覆盖最常见、最基础的概率题型:计数法单事件、补事件、独立事件组合(交集/并集)与互斥事件并集。
    This is a beginner-friendly probability calculator covering the most common basics: counting-based single event, complement, independent-event combinations (intersection/union), and mutually exclusive union.
  • 单事件计数法 · Single event by counting:
    当题目明确说“等可能(equally likely)”或可合理假设每个结果概率相同,才使用:
    P(A) = k / n,其中 k 为有利结果数,n 为总结果数。
    Use counting only when outcomes are equally likely: P(A)=k/n, where k=favorable outcomes and n=total outcomes.
    例子 · Example: 一次掷骰子,出现偶数的概率:k=3(2/4/6),n=6 → P=3/6=1/2。
  • 补事件(对立事件) · Complement:
    很多题不直接算“发生”,反而更容易算“不发生”。补事件公式是:
    P(Aᶜ) = 1 – P(A)
    If it’s easier to compute “not happening”, use: P(Aᶜ)=1-P(A).
    例子 · Example: 骰子不出现 6:P=1-1/6=5/6。
  • 独立事件 · Independent events:
    独立的直觉:A 发生与否不会改变 B 的概率。常见于“重复试验”(多次抛硬币、多次抽球且放回等)。
    The intuition: whether A happens does not affect the chance of B. Common in repeated trials (coin flips, sampling with replacement, etc.).
    公式:
    P(A∩B)=P(A)·P(B)(交集 / both happen)
    P(A∪B)=P(A)+P(B)-P(A)·P(B)(并集 / at least one happens)
    小提醒 · Tip: “并集”不是简单相加,除非它们互斥。Union is not simply addition unless mutually exclusive.
  • 互斥事件 · Mutually exclusive:
    互斥的直觉:A 与 B 不可能同时发生(同一次试验中二选一)。
    The intuition: A and B cannot both happen in the same trial (either-or).
    公式:P(A∪B)=P(A)+P(B)
    注意:互斥 ≠ 独立。若 A、B 互斥且 P(A)、P(B) 都大于 0,则它们一定不独立。Mutually exclusive events (with nonzero probabilities) are never independent.
  • 输入格式 · Input format:
    你可以在 P(A)、P(B) 输入框里写:0.2525%;工具会自动识别并转换为 0~1 的概率值。
    You can type 0.25 or 25%; the tool will parse it into a probability between 0 and 1.
  • 结果解读 · How to read results:
    本工具同时输出小数、百分比与(尽量)分数形式:
    – 若来源是“有利/总数”,分数是精确约分(例如 3/6 → 1/2)。
    – 若来源是小数/百分数输入,分数是基于小数的近似转换(可能存在微小误差)。
    This tool outputs decimal, percentage, and a (best-effort) fraction. Counting-based fractions are exact; decimal-based fractions may be approximate.
  • 重要限制 · Important limitations:
    本工具不自动判断题目是否“真的独立/真的互斥”,因为这取决于题意与实验设计;你需要先理解场景再选模式。
    The tool does not decide whether events are truly independent or mutually exclusive—this depends on the problem setup. You choose the mode based on understanding.

免责声明:本工具用于学习与快速验算,不能替代完整的概率建模与推理过程。若题目涉及条件概率、贝叶斯、组合计数、抽样不放回等更复杂情况,建议先列出事件空间与条件,再计算。
Disclaimer: This tool is for learning and quick checking. For advanced topics (conditional probability, Bayes, combinatorics, sampling without replacement), write down the sample space and conditions first.