power.prop.test 中的默认功率应该是多少？
What should the default power be in power.prop.test?
power.prop.test 的功率部分是否有行业标准？
我正在使用函数查找 p2 但不确定功率的标准是什么。
power.prop.test(
n= 6289195,
p1=0.004,
power=0.8,
sig.level=0.05,
tol=.Machine$double.eps^.8)
例如，应该是0.8还是0.9？
这是一个实用的统计问题，而不是R问题，但0.8的幂，即80%是常见的。由于它很常见（而不是 95% 的置信度），人们认为他们理解它在说什么，并且不会像查询其他值那样质疑它的选择。
您需要记住它是一个任意目标：如果您在您的示例中更改它，那么主要影响将是为您提供 p2 的不同结果。实际上，您应该明确平衡增加样本量的成本与 I 类错误和特定 II 类错误的不同成本
常见的参考文献是 Cohen J. (1988)。 行为科学的统计功效分析。纽约州纽约市：Routledge Academic，第 2.4 节，其中说：
It is proposed here as a convention that, when the investigator has no other basis for setting the desired power value, the value .80 be used. This means that b is set at .20. This arbitrary but reasonable value is offered for several reasons (Cohen, 1965, pp. 98-99). The chief among them takes into consideration the implicit convention for a of .05. The b of .20 is chosen with the idea that the general relative seriousness of these two kinds of errors is of the order of .20/.05, i.e., that Type I errors are of the order of four times as serious as Type II errors. This .80 desired power convention is offered with the hope that it will be ignored whenever an investigator can find a basis in his substantive concerns in his specific research investigation to choose a value ad hoc.
在快速搜索中找到的 0.8 的其他示例：
- R stats reference page for power.prop.test以
power=0.8为例
- 一个University of Ottawa medicine page说"A power of 80% is often chosen; hence a true difference will be missed 20% of the time. This is a compromise because raising power to 90% power will require increasing the sample size by about 30%"
- Statistics Done Wrong 网站和书籍说 "A scientist might want to know how many patients are needed to test if a new medication improves survival by more than 10%, and a quick calculation of statistical power would provide the answer. Scientists are usually satisfied when the statistical power is 0.8 or higher, corresponding to an 80% chance of concluding there’s a real effect. However, few scientists ever perform this calculation, and few journal articles ever mention the statistical power of their tests."
power.prop.test 的功率部分是否有行业标准？
我正在使用函数查找 p2 但不确定功率的标准是什么。
power.prop.test(
n= 6289195,
p1=0.004,
power=0.8,
sig.level=0.05,
tol=.Machine$double.eps^.8)
例如，应该是0.8还是0.9？
这是一个实用的统计问题，而不是R问题，但0.8的幂，即80%是常见的。由于它很常见（而不是 95% 的置信度），人们认为他们理解它在说什么，并且不会像查询其他值那样质疑它的选择。
您需要记住它是一个任意目标：如果您在您的示例中更改它，那么主要影响将是为您提供 p2 的不同结果。实际上，您应该明确平衡增加样本量的成本与 I 类错误和特定 II 类错误的不同成本
常见的参考文献是 Cohen J. (1988)。 行为科学的统计功效分析。纽约州纽约市：Routledge Academic，第 2.4 节，其中说：
It is proposed here as a convention that, when the investigator has no other basis for setting the desired power value, the value .80 be used. This means that b is set at .20. This arbitrary but reasonable value is offered for several reasons (Cohen, 1965, pp. 98-99). The chief among them takes into consideration the implicit convention for a of .05. The b of .20 is chosen with the idea that the general relative seriousness of these two kinds of errors is of the order of .20/.05, i.e., that Type I errors are of the order of four times as serious as Type II errors. This .80 desired power convention is offered with the hope that it will be ignored whenever an investigator can find a basis in his substantive concerns in his specific research investigation to choose a value ad hoc.
在快速搜索中找到的 0.8 的其他示例：
- R stats reference page for power.prop.test以
power=0.8为例 - 一个University of Ottawa medicine page说"A power of 80% is often chosen; hence a true difference will be missed 20% of the time. This is a compromise because raising power to 90% power will require increasing the sample size by about 30%"
- Statistics Done Wrong 网站和书籍说 "A scientist might want to know how many patients are needed to test if a new medication improves survival by more than 10%, and a quick calculation of statistical power would provide the answer. Scientists are usually satisfied when the statistical power is 0.8 or higher, corresponding to an 80% chance of concluding there’s a real effect. However, few scientists ever perform this calculation, and few journal articles ever mention the statistical power of their tests."