A Beginner's Guide to Standardizing Data in Excel

Excel continues to be a versatile tool for handling data analysis tasks in all industries. In 2023, there were estimated to be between 1.1 and 1.5 billion Microsoft Excel users worldwide. 

The STANDARDIZE function enables users to standardize dataset values for easier comparison. Whether you're a financial expert, data analyst, or researcher, understanding the STANDARDIZE function can help you learn about data more effectively, especially when working with different datasets of varying sizes.

This blog discusses how to use the STANDARDIZE function, its practical uses, and how it can improve your Excel workflow.

What Is the STANDARDIZE Function in Excel?

STANDARDIZE normalizes data points inside a dataset and converts them to Z-scores. Z-scores indicate how many standard deviations a specific number is from the dataset's mean (average). This transformation is required for normalizing data that would otherwise be challenging to compare due to discrepancies in units or scale.

Simply put, standardization assists in leveling the playing field among datasets, helping analysts compare numbers consistently. For example, if you’re comparing sales numbers from different parts of the globe, standardization can help you to account for variables such as currency disparities or population sizes, making the comparison more precise and meaningful.

The STANDARDIZE function requires three pieces of information:

• x: The precise data point you wish to normalize.
• Mean: The dataset's mean value.
• Standard_dev: The standard deviation, which determines how far off the data points are from the mean.

The function is written as:

=STANDARDIZE(x, mean, standard_dev)

You can use this function to produce a Z-score, which informs you how far a particular number is from the mean and is expressed in standard deviations.

To further understand the STANDARDIZE function, let's look at the underlying math.

How Does the Standardize Function Work?

When you use this function, it utilizes the following formula:

Z = (x - mean) / standard_dev

We subtract the mean from the data point to calculate the value's deviation from the average. Then, dividing by the standard deviation scales the deviation to show how distant it is from the mean.

This standardized outcome (Z-score) indicates whether a number is higher or lower than the mean and by how much. A Z-score of zero indicates that the data point is precisely at the mean. A Z-score of 1 indicates that the value is one standard deviation higher than the mean, whereas a Z-score of -1 indicates that the value is one standard deviation lower.

Now that you understand how the STANDARDIZE function works, let’s explore some common scenarios where it can be effectively applied.

Applications of the STANDARDIZE Function

This function is frequently used in a variety of industries, including business and finance, healthcare, and scientific research. 

One of the most common uses is to compare data from multiple sources or measurement units. Assume you're assessing the success of sales teams across multiple regions, each presenting their data in different currencies. To establish meaningful comparisons, the STANDARDIZE function normalizes the data, eliminating the impact of currency changes and regional disparities and enabling you to compare data on an equal scale.

Another useful application is to detect outliers in your data. Outliers are data points that differ dramatically from the majority of the data and may bias your research. You can find values that are abnormally high or low in relation to the rest of the dataset by computing Z-scores for every data point. A Z-score higher than 3 or lower than -3 usually suggests an outlier.

When displaying data, standardizing it often results in clearer and more understandable graphs and charts. You can plot the data distribution more precisely by converting your data into Z-scores. This is especially beneficial when comparing the distribution of values across multiple datasets or attempting to understand variance within a dataset.

Standardizing data is crucial for comparing performance measures between businesses of various sizes in the finance industry. Return on investment (ROI), for instance, may be computed for multiple businesses, although the precise figures may change because of the businesses' varying sizes. Standardization facilitates the comparison of their respective performances and helps normalize these discrepancies.

Standardization can be very important in medical research when comparing test findings from various populations or assessing variables that are affected by diverse causes. To provide a fair comparison, researchers may utilize the STANDARDIZE function to account for various age groups or individuals when comparing health measures.

Also Read: Mastering Data Validation in Google Sheets: Essential Tips for Beginners

Step-by-Step Guide to Using the STANDARDIZE Function in Excel

Excel's STANDARDIZE function is fairly easy to use, but maximizing its potential requires understanding the precise procedures and how to use them correctly. 

Step 1 - Understand the Formula Syntax

Before we get started, it's crucial to understand the syntax of the STANDARDIZE function. The formula needs three arguments:

=STANDARDIZE(x, mean, standard_dev)

Step 2 - Organize Your Data

Begin by arranging your data in Excel. The STANDARDIZE function is most useful when you have a set of values and wish to standardize each one based on the range's mean and standard deviation.

Here’s an example dataset:

In this example, the Value column includes the raw data points, on which we will use the STANDARDIZE function.

Step 3 - Calculate the Mean of the Dataset

Apply the AVERAGE function to determine the dataset's arithmetic mean. Assuming the data is in column A, type the following formula in an empty cell:

=AVERAGE(A2:A6)

This calculates the average of all the numbers in the dataset (in this instance, 100, 120, 130, 110, and 150). In this instance, the dataset mean is 122.

Step 4 - Calculate the Standard Deviation

Next, estimate the dataset's standard deviation. The standard deviation indicates how distributed the numbers are. The standard deviation for a whole population can be determined using Excel's STDEV.P function. Fill out the next formula in a blank cell:

=STDEV.P(A2:A6)

This will provide you with the dataset's standard deviation. Assume the outcome of the calculation is 20. This means that the values vary by 20 units on average.

Step 5 - Apply the Standardize Function

Now, execute the STANDARDIZE function to standardize each data point. To accomplish this, insert the formula into another column next to the original data. Let's suppose you want to normalize the value in cell A2 (100). Click on the cell where you wish the standardized value to show up (say, B2), and input the following formula:

=STANDARDIZE(A2, $B$7, $B$8)

In this case:

- A2 is the value you wish to standardize in this example (100).

- $B$7 is the dataset's mean (as calculated earlier).

- $B$8 represents the dataset's standard deviation (which was also determined earlier).

After you hit Enter, you should get the Z-score (standardized value) for data point 100. The algorithm essentially turns 100 into a Z-score, which indicates how far the result deviates from the mean as a matter of standard deviations.

Repeat this step for all of the other values in your dataset to create a standardized values column.

Step 6 - Review the Results

Upon using the STANDARDIZE function across the entire range of data, the end result might look somewhat like this.

In this case, the standardized values indicate the number of standard deviations for every value that deviates from the mean. For example, a value of 100 is 1.1 standard deviations lower than the mean of 122, but 150 is 1.3 standard deviations higher.

Step 7 - Interpret the Standardized Data

After you've normalized your dataset, you can analyze your results. The STANDARDIZE function highlights each data point's relative position within the context of the overall dataset. A Z-score of zero indicates that the value is exactly at the mean, whereas positive and negative Z-scores signify values that are above and below the mean, respectively.

Limitations of Excel's STANDARDIZE Function

While Excel's STANDARDIZE function is a useful tool for normalizing data, it's crucial to acknowledge its limitations before using it in every situation. Identifying why and how the STANDARDIZE function may fail will allow you to make better decisions about your data analyses. 

Assumption of a Normal Distribution

One of the primary requirements for using the STANDARDIZE function is that the data implies a normal distribution, commonly known as a bell curve. The normal distribution is a distribution of probabilities in which the majority of data points cluster around the mean, with fewer values occurring as you go away from the center.

When you standardize data, the function determines how distant a given data point is from the mean, as measured in standard deviations. This strategy is most effective when your data closely resembles a normal distribution, as it presumes that the mean and standard deviation are adequate to characterize the data's spread.

However, if your dataset is severely skewed or has a distribution that is not normal (for example, a bimodal distribution with two separate peaks), the Z-scores computed by the STANDARDIZE function may not adequately reflect the data distribution. In these circumstances, different procedures may be required to produce a more precise representation of your data.

Challenges With Missing or Non-Numeric Data

The STANDARDIZE function demands numerical data to complete its calculations. If your dataset has any missing or non-numeric values, Excel will receive an error when attempting to utilize the STANDARDIZE function. This is a common issue when dealing with real-world datasets, as the data is sometimes inadequate or contains categories of variables that cannot be used for mathematical operations like normalization. Excel will not be able to handle values in a column that contains text, blank cells, or symbols and will raise an error. 

Necessity for Data Cleaning Before Usage

As previously stated, data cleanliness is critical when utilizing the STANDARDIZE function. Raw datasets frequently contain irregularities, such as inaccurate values, missing data, outliers, and formatting variations, which can impair the function's accuracy. If these flaws are not rectified prior to running the function, you may get skewed results or calculation errors.

While the STANDARDIZE function is straightforward to use, challenges can arise, especially when working with large or messy datasets. Let’s dive deeper into some common issues users face and explore practical solutions to address them effectively.

Common Errors and Troubleshooting

The #NUM! error is one of the most common errors encountered while using the STANDARDIZE function. This happens when the standard_dev option is less than or equal to 0. Standard deviation is an estimation of variability in a dataset, and it cannot be 0 or negative.

If you see a #NUM! issue, verify the standard deviation you are using. This inaccuracy results from the standard deviation being 0 if the dataset contains identical values. This can be addressed by either:

Modifying your Data: If all of your data points are identical, standardization may be ineffective. Consider whether you should re-evaluate your dataset.

Employing a More Appropriate Approach: If your dataset has low variance, Z-scores may not be relevant. In such circumstances, consider employing other statistical approaches that are more appropriate for your data's features.

The #VALUE! error is the next most common type of error that occurs in this process. This error usually arises when the STANDARDIZE function meets a non-numerical value in the arguments. Because the STANDARDIZE function needs numerical data, any text, blank cells, or symbols used as parameters will result in this error.

To troubleshoot this, make sure that the function you're using has numeric values for the mean, standard_dev, and x. The STANDARDIZE function in Excel does not allow non-numeric characters or text as valid input. Before using the function, utilize Excel's built-in data validation features to confirm that all of the cells involved have valid numeric values. You can also apply the ISNUMBER() function to see if your values are numeric before using STANDARDIZE. 

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Tired of dealing with messy, time-consuming data tasks? Savant simplifies standardization with intuitive automation, saving you time and ensuring accuracy. Ready to take your data analysis to the next level? Explore how Savant can transform your workflow today!

Also Read: Practical Guide to Using Advanced Data Analytics