Power And Precision 統計分析軟體

■Power Analysis

能力分析的目標是透過考慮到研究的實質性目標, 在這些原素中找到適當的平衡， 以及找出對研究人員可提供的資源。
Traditionally, data collected in a research study is submitted to a significance test to assess the viability of the null hypothesis. The p-value provided by the significance test, and used to reject the null hypothesis, is a function of three factors: The larger the observed effect, the larger the sample size, and/or the more liberal the criterion required for significance (alpha ), the more likely it is that the test will yield a significant p-value.

A power analysis, executed when the study is being planned, is used to anticipate the likelihood that the study will yield a significant effect and is based on the same factors as the significance test itself. Specifically, the larger the effect size used in the power analysis, the larger the sample size, and/or the more liberal the criterion required for significance (alpha), the higher the expectation that the study will yield a statistically significant effect.

These three factors, together with power, form a closed system - once any three are established, the fourth is completely determined. The goal of a power analysis is to find an appropriate balance among these factors by taking into account the substantive goals of the study, and the resources available to the researcher.

■ Role of Effect Size in Power Analysis

影響力的大小描述將具有臨床或者實質性的意義的最小的效應， 並且因此它將從一項研究變化到下一個。例如， 治療會考慮疾病的嚴重性，把死亡率減少重於減少氣喘病這個副作用。
考慮交替的處理方法，也需考慮處理的費用和副作用(帶這些負擔的一種處理方法將被採用， 處理效應非常實際) .
權力分析可以適當分析具體影響大小的權力。例如:權力報告" 如果處理增加恢復速率20個百分點，研究將有80%的能力產生一種重要的效應"。
小效應將比大效應要求資源的更大投資。

■Precision Analysis

我們將能報告比率的精密差別是一個需要的可信水準的功能， 樣品量和結果索引的變化。可靠區間代表我們能報告影響尺寸的精密，並且這個樣品越大，估計越準確。

 The discussion to this point has focused on power analysis, which is the logical precursor to a test of significance. If the researcher designing a study to test the null hypothesis, then the study design should ensure, to a high degree of certainty, that the study will be able to provide an adequate (i.e. powerful) testing of the null hypothesis.

The study may be designed with another goal as well. In addition to (or instead of) testing the null hypothesis the researcher might use the study to estimate the magnitude of the effect - to report, for example that the treatment increases the cure rate by 10 points, or by 20 points, or by 30 points. In this case, study planning would focus not on the study's ability to reject the null hypothesis but rather on the precision with which it will allow us to estimate the magnitude of the effect.

Assume, for example, that we are planning to compare the response rates for treatments, and anticipate that these rates will differ from each other by 20 percentage points. We would like to be able to report the rate difference with a precision of plus/minus 10 points.

The precision with which we will be able to report the rate difference is a function of the confidence level required, the sample size, and the variance of the outcome index. Except in the indirect manner discussed below, it is not affected by the effect size.