# Understanding the Mode – Statistics 101

Understanding the mode is important. The mode, or the most occurring value in a data set, is a commonly used descriptive statistic that is especially useful when working with nominal or categorical data.

The video below provides a brief overview of what the mode is, answers to frequently asked questions (FAQs) about the mode and gives real world examples of using the mode.

The mode is different than the mean (average) and median. The mode is the most occurring value whereas the mean is the sum divided by the number of observations in the data set and the median is the middle number in the data set when the data is put into order by size, from smallest to largest.

All of the data in the data set has to be in or converted to the same unit of measurement in order to find the mode.

There can be more than one mode in a data set. Some data sets may have multiple values that occur the most.

Watch the above video to see how the mode is used to find the state(s) with the most customer orders in the e-commerce store example, and how it’s used to in the plant nursery/garden store example to identify the type of flower or plant that survey respondents like to give as gifts.

In conclusion, the mode is useful in a variety of different types of data sets in a variety of different scenarios across many industries. Refreshing your understanding of this basic descriptive statistic will help you use it in ways that are useful to you and your organization.

Do have any examples of how the mode is useful in analyzing data for your work or in your life? Share your example in the comments below!

# 4 Things to Avoid with Data Spreadsheet Column Titles

Avoiding these 4 things in data spreadsheet column titles, as discussed in the quick (3 minute and 38 second) video below and in this post, will make it easier to analyze the spreadsheet data using programs like R or SAS or importing the spreadsheet data into a database.

(1) Avoid fancy formatting

Avoid fancy formatting in the column headers when the spreadsheet is going to be imported into a program such as R or SAS for analysis.

Fancy formatting such as making words bold, italic, or adding colors can cause errors or even prevent the data from being able to be imported until the formatting is reverted back to plain, unformatted column headers.

Watch the video above to see examples of fancy and plain column headers.

Avoiding using fancy formatting in the first place will save your consultant or analyst time from having to change the formatting back to plain.

(2) Avoid inconsistent naming conventions

Avoid inconsistent naming conventions for titles and also avoid using special characters such as exclamation points(!), asterisks(*), at symbols (@), and other such symbols in the title of the column header.

For one it can be confusing. Also, special characters in the titles can cause errors in analysis or importing the data into a database.

For an example of a spreadsheet using inconsistent naming conventions and special symbols in the column headers watch the video above.

(3) Avoid using really long descriptive titles

Avoid using long descriptive titles with spaces in between the words. Shorter titles are easier to analyze. Some software programs can’t recognize titles that have spaces between the words as variables. This will result in error messages or a failure to import the data.

Shorten the titles using codenames for those variables.

For an example of really long descriptive titles being shortened into code names, watch the video above.

Shortening codenames leads to the 4th thing to avoid in column title headers:

(4) Avoid forgetting to include a code book or some kind of documentation of what code names you shortened the long descriptive titles into

Don’t forget to include a code book or other similar documentation for long descriptive column titles that you’ve shortened the name of.

When working with a team no one else will necessarily know what that code title means and if you don’t document it somewhere, then you may even forget what it was yourself.

By avoiding fancy formatting, inconsistent naming conventions, using really long descriptive titles, and forgetting to include a codebook or other documentation you will make it easier and more efficient to analyze the spreadsheet data using programs like R or SAS or importing the spreadsheet data into a database.