hypergeometric 的音標是[?ha?p?r?d???m?t?k]，基本翻譯是超幾何學的，速記技巧是“海菊”=hypergeometric（超幾何學的）。

Hypergeometric這個詞來源于希臘語，意為“超越幾何的”。它的變化形式包括hypergeometrician（超幾何學家），hypergeometricianism（超幾何學派）。相關單詞有：

1. Hypergeometric series（超幾何級數）：一種在數學中用于描述特定形狀或分布的公式，特別是在統計和概率論中。

2. Hypergeometric distribution（超幾何分布）：一種統計分布，用于從給定數量的成功和失敗的獨立試驗中估計概率。

3. Hypergeometric probability（超幾何概率）：一種特殊的概率分布，用于描述在有限樣本空間中隨機抽取特定樣本的條件下的事件的概率。

4. Hypergeometric function（超幾何函數）：一種數學函數，用于描述超幾何分布的概率密度。

5. Hypergeometric distribution curve（超幾何分布曲線）：一種描述超幾何分布的概率密度的曲線。

這些單詞都與超幾何學有關，是數學和統計學中的重要概念。它們在科學研究和實際應用中發揮著重要作用。

常用短語：

1. hypergeometric distribution

2. hypergeometric probability

3. hypergeometric sample

4. hypergeometric model

5. hypergeometric distribution formula

6. hypergeometric random variable

7. hypergeometric theory

例句：

1. The hypergeometric probability of drawing a red ball from a bag containing 10 blue and 20 red balls is 70%.

2. The hypergeometric model is widely used in biological and medical research.

3. The hypergeometric distribution formula is simple and easy to use.

4. Hypergeometric random variables are commonly used in statistical analysis.

5. Hypergeometric theory is a fundamental part of mathematical statistics.

英文小作文：

Hypergeometric Theory: An Introduction

Hypergeometric theory is a fundamental part of mathematical statistics, playing a crucial role in many areas such as biological and medical research, genetic engineering, and more. It is a statistical model that assumes that the probability of an event happening depends on the relative frequency of the event in a large population of similar events.

In the hypergeometric model, a sample is drawn from a larger population, and the probability of drawing an item from the sample is determined by the relative sizes of the two populations and the number of items in the sample. This model is particularly useful for situations where the number of items in the larger population is large compared to the number of items in the smaller population, and where the items in the smaller population are relatively rare compared to the items in the larger population.

For example, imagine a farmer who has 100 apples and 20 oranges in a bag, and wants to draw a fruit from the bag to eat. The hypergeometric model can be used to determine the probability of drawing an apple or an orange, given that there are more oranges than apples in the bag, and that there are only a limited number of fruits in the bag to draw from. This model can also be extended to more complex situations where multiple items are drawn from multiple populations, allowing for more accurate predictions and analysis of complex data sets.