Simpson's Paradox is a paradox British statistician EH Simpson proposed in 1951 that the two sets of data under certain conditions, when will meet separately to discuss certain properties, but once the merger consideration, it may lead to the opposite conclusion.

A college two American universities, namely:

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Law and business schools, the new semester enrollment. It is suspected that two Academy sexist, now make the following statistics:

We can see from the data shown on FIG, law school boys acceptance rates of 8/53 = 15.1%, the proportion of female enrolled 51/152 = 33.6%. Similarly, the business school enrollment ratio of boys is 80.1%, the proportion of girls enrolled was 91.1%.

Both in law school or business school, the proportion of girls are enrolled than boys, it can be inferred when school enrollment more inclined to recruit girls do?

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Admissions school when calculating the ratio of male was admitted to 209/304 = 68.8%, the proportion of female enrolled 143/253 = 56.5%. Acceptance rate to boys than girls, which, I am afraid to turn the girls feel the injustice.

So the question is: the university's admissions policy, in the end there is no gender discrimination? In the end is discrimination boys or girls?

I will not speak conclusion, we will look at a practical work will encounter case.

Work in a typical case :

The user of a product's 10,000 people use Android devices using IOS device 5000, the overall conversion rate of pay should be 5%. Conversion was found broken IOS device 4%, and Android device is 5.5%. "Smart" data analyst concluded: IOS platform users pay conversion rate is low, it is recommended to give up IOS platform development.

In general, the conversion of IOS flat fee is much higher than Android tablet, and the conversion of IOS phones is relatively better. In this case, the device type is a complex variable, if the data is obtained based on device type, then the other data can be completely ignored.

Now let's compare this set of data:

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Thus, Android device conversion rate in terms of flat side or in the conversion rate of less than IOS mobile terminal equipment, which is very consistent with our conventional expectations.

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• 用户量：免费产品需要很大的用户量才能获得足够的总收入，因为该模式的转化率极低。而这些用户通常来自全球各个地区，使用各种不同类型的设备。针对不同的设备类型，采用通用的平均值是没有意义的。
• LTV范围：免费产品需要很长的货币化周期，把用户消费当作玩家是否开心的依据，就像参与度和消费紧密相关一样，因此可以作为分类的标准。

A/B****测试中的注意点

• DNU，Daily New Users：每日新增用户
• AU，Active User：活跃用户，统计特定周期内完成过指定事项或指标的用户数
• PU，Paying User：付费用户
• APA，Active Payment Account：活跃付费用户数
• ARPU，Average Revenue Per User：平均每用户收入，总收入/AU
• ARPPU，Average Revenue Per Paying User：平均每付费用户收入，总收入/APA
• PUR，Pay User Rate：付费比例，APA/AU
• LTV, Life Time Value: Lifetime Value

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Origin blog.csdn.net/weixin_33895695/article/details/90882933
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