Formulas explained

As a marketer, I’m constantly looking for ways to streamline my work. That’s why I’m a huge fan of Google Sheets. This powerful spreadsheet tool helps me keep track of everything from my budget to my editorial calendar. And one of the features that makes Sheets stand out is its ability to use formulas to automate tasks and make my work even easier. Today, I’m going to take a deep dive into one of my favorite Sheets formulas: NORMSINV.

If you’re not a math wizard, the name “NORMSINV” might sound intimidating. But don’t worry – it’s actually a very simple formula. NORMSINV is short for “normal inverse.” In plain English, that means it helps you find the z-score for a given probability. Still confused? Let me break it down even further.

In statistics, a z-score is a measure of how far a particular data point is from the mean of a data set. For example, let’s say I have a list of email open rates for a campaign. If I want to know how above or below average a particular open rate is, I can calculate the z-score for that rate.

So, how does NORMSINV help with this? To put it simply, you use NORMSINV to find the z-score for a given probability. In other words, if you know what percentage of the data falls above or below a certain value, NORMSINV can tell you how many standard deviations from the mean that value is.

Now that we know what NORMSINV does, let’s dive into how to use it in Google Sheets. Here’s the basic syntax:

`=NORMSINV(probability)`

As you can see, NORMSINV takes one argument: probability. This is the percentage of the data that falls below a certain value. For example, let’s say I have a list of email open rates for a campaign. If I want to know the z-score for an open rate that falls in the top 10% of all open rates, I would use the following formula:

`=NORMSINV(0.1)`

And that’s it! Sheets will return the z-score for that open rate. Now, what can you do with that z-score? Let’s take a look at a few use cases.

One way to use NORMSINV is to identify outliers in your data. As I mentioned earlier, a z-score measures how far a particular data point is from the mean of a data set. Generally speaking, if a z-score is greater than 2 (or less than -2), that data point is considered an outlier.

Let’s say I have a list of email open rates for a campaign. I can use NORMSINV to find the z-score for each open rate. Then, I can use conditional formatting to highlight any cells where the z-score is greater than 2 or less than -2. This makes it easy to quickly identify any outliers in my data.

Another way to use NORMSINV is to compare data sets. Let’s say I have two different email campaigns, and I want to compare their open rates. The problem is, the campaigns have different sample sizes, so I can’t just compare the raw numbers. But if I calculate the z-scores for each open rate using NORMSINV, I can compare the z-scores directly.

For example, let’s say Campaign A has an open rate of 25% and Campaign B has an open rate of 30%. At first glance, it might seem like Campaign B is the clear winner. But if I calculate the z-scores for each open rate, I might find that Campaign A’s open rate is actually more impressive. If Campaign A has a larger sample size than Campaign B, it’s possible that its open rate is actually farther from the mean than Campaign B’s open rate.

Finally, NORMSINV can be used to set benchmarks for your data. Let’s say I want to set a benchmark for email open rates. I can use historical data to calculate the mean and standard deviation of all my past open rates. Then, I can use NORMSINV to find the z-score for any open rate. If the z-score is greater than 1.96 (or less than -1.96), I know that open rate falls outside the range of what I would consider “normal.” This can help me set a realistic goal for future open rates, based on historical data.

NORMSINV might sound intimidating, but it’s actually a very simple formula that can be incredibly powerful in the right hands. Whether you’re looking to identify outliers, compare data sets, or set benchmarks, NORMSINV can help. And thanks to Google Sheets, it’s easy to use this formula in your day-to-day work as a marketer (or whatever else you do!). So go ahead – give NORMSINV a try, and see how it can help you make more informed decisions based on data.