# Precision and consistency of treatment effects

## 95% confidence intervals are dependent upon sample size

**95% confidence interval**wrapped around the findings of inferential analyses.

__is not an exact mathematical science as far as other exact mathematical sciences go,__

**Statistics****measurement error**is inherent when attempting to measure for anything related to human beings, and

**FEW tried and true causal effects**have been proven scientifically. Statistics'

**strength as a mathematical science**is in its

**ability to build confidence intervals around findings to put them into a relative context**.

Also, 95% confidence intervals act as the

**primary inference associated with unadjusted odds ratios, relative risk, hazard ratios, and adjusted odds ratios**. If the confidence interval crosses over 1.0, there is a non-significant effect. Wide 95% confidence intervals are indicative of small sample sizes and lead to decreased precision of the effect. Constricted or narrow 95% confidence intervals reflect increased precision and consistency of a treatment effect.

In essence,

**. The interpretation of your findings**

*p*-values should not be what people get excited about when it comes to statistical analyses**within the context of the subsequent population means, odds, risk, hazard, and 95% confidence intervals**IS the real "meat" of applied statistics.