# Power Pivot Principles: The A to Z of DAX Functions – CONFIDENCE.NORM

14 June 2022

*In our long-established Power Pivot Principles articles, we continue our series on the A to Z of Data Analysis eXpression (DAX) functions. This week, we look at CONFIDENCE.NORM.*

*The CONFIDENCE.NORM function*

Is **CONFIDENCE** your **NORM**? This DAX function returns the confidence interval for a population mean, using the theory associated with the Normal distribution.

To explain: the confidence interval is a range of values around a sample mean, **x**, which sits at the centre of this range, *i.e. *the range is **x** ± **CONFIDENCE.NORM**. For example, if **x** is the sample mean of delivery times for products ordered through the mail, **x ± CONFIDENCE.NORM** is a range of population means.

For any population mean, **μ**** _{0}**, in this range, the probability of obtaining a sample mean further from

**μ**

**than**

_{0}**x**is greater than the significance level required,

**alpha**; for any population mean,

**μ**

**, not in this range, the probability of obtaining a sample mean further from**

_{0}**μ**

**than**

_{0}**x**is less than this level,

**alpha**. In other words, assume that we use

**x**, a standard deviation

**standard_dev**, and

**size**to construct a two-tailed test at significance level alpha of the hypothesis that the population mean is

**μ**

**. Then we will not reject that hypothesis if**

_{0}**μ**

**is in the confidence interval and will reject that hypothesis if**

_{0}**μ**

**is not in the confidence interval. The confidence interval does not allow us to infer that there is probability**

_{0}**1 – alpha**that our next package will take a delivery time that is in the confidence interval.

The **CONFIDENCE.NORM **function employs the following syntax to operate:

**CONFIDENCE.NORM**(**alpha**, **standard_dev**,** size**)

**alpha:**this is required. This represents the significance level used to compute the confidence level. The confidence level equals**100*(1 - alpha)%**, or in other words, an**alpha**of 0.05 indicates a 95 percent confidence level**standard_dev:**this is also required. This is the population standard deviation for the data range and is assumed to be known**size:**also required. This is the sample size.

It should be further noted that:

- if any argument is non-numeric,
**CONFIDENCE.NORM**returns the*#VALUE!*error value - if
**alpha**is ≤ 0 or ≥ 1,**CONFIDENCE.NORM**returns the*#NUM!*error value - if
**standard_dev**≤ 0,**CONFIDENCE.NORM**returns the*#NUM!*error value - if
**size**is not an integer, it is truncated - if
**size**< 1,**CONFIDENCE.NORM**returns the*#NUM!*error value

if we assume **alpha **equals 0.05, we need to calculate the area under the standard normal curve that equals **1 - alpha**, or 95%. This value is ± 1.96. The confidence interval is therefore:

- this function is not supported for use in DirectQuery mode when used in calculated columns or row-level security (RLS) rules.

Please see my example below:

*i.e.* for an **alpha **of 5% (or a confidence interval of 95%), with a standard deviation of the population of 3.0 and a sample size of 100 (for a Normal distribution), the confidence interval around the sample mean will be 0.5880.

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