Statistics are a valuable asset for content marketers. They offer insight to readers on how your thoughts relate to well-researched studies.
In doing so, statistics can give credibility to your blog posts. With that newfound credibility, your audience has another reason to trust your opinions.
But stats can easily be taken out of context. You may analyze the data without all the facts. Or the research report may lead you to incorrectly interpret the stats.
Let’s learn more about statistics and how you can accurately interpret stats to improve your writing skills.
What are Statistics?
A statistic is a piece of data that conveys a numerical value of research. Statistics appear in economics, cultural trends, and research studies. Many companies conduct surveys with their customers to produce annual industry reports.
Statistics provide a way for the reader to learn from data. Sometimes, content marketers use stats to inform, persuade, or entertain their audience. For example, Spotify gives its customers an individualized list of their top songs of the year. This type of data visualization is especially useful for eCommerce websites.
The beauty of using statistics in your writing is that it can simplify complex scenarios. Industry reports with stats hold a unique opportunity for you to share relevant insights along with your thoughts.
Statistics from Original Sources
Finding the original source is the first step in interpreting statistics. It’s a verifiable way to ensure your audience gets accurate information. So, you want to avoid citing statistics from roundup posts that link to other roundup posts. Or linking to a shady third-party site that will vanish in a few months.
The original source is usually a PDF version of the actual report. You can either link to the lead generation form page for readers to access the report. Or you can link directly to the PDF report.
The original source is also for you to gain context about the stat. You don’t want to use someone else’s interpretation. Katelyn Bourgoin, CEO of Customer Camp, says, “Data without context is just noise.” As a content marketer, you’re responsible for educating your readers about the stats in your blog posts.
There are a few drawbacks to not using original sources in your content. Without a link to the original source, your readers may lose credibility in your work. They have no way of knowing whether you’re making up the stat or not.
Second, a link to another blog post that cites the stat only leaves your readers with more questions than answers. Now, they’re tasked with going down the proverbial rabbit hole to find the data.
When in doubt, cite the original source. It’s a sure-fire way to build trust with your readers.
Reviewing the Methodology
A research report is only as good as its methodology. To grasp the full context of a statistic, you’ll want to review the methodology section of the report. It’s usually located after the table contents or in the appendix.
The methodology offers details about who conducted the survey, the demographics of the survey participants, and the date the survey data was collected. Some reports will include insight on how they collected the data and the definitions for the terminology.
In general, a research report uses a sample size of a specific population. The goal of a study is to extrapolate the behaviors of the larger population. There’s also a big difference between a study of 10,000 people versus 100 people; fewer study participants makes it difficult to reflect the larger population’s behaviors.
A common misconception in statistical reporting is that one study proves or confirms anything. You need a series of studies with similar outcomes to develop a stronger argument. Otherwise, a single study with a limited sample won’t fully support your claim.
Common Statistic Mistakes Made By Content Marketers
Content marketers are human. We all make mistakes in the writing process. The good news is you can improve your writing skills through self-awareness and knowledge. Before you quickly add a statistic to your next blog post, consider avoiding the following common mistakes.
Data distortion is the misrepresentation of data. Whether it’s intentional or unintentional, data distortion fails to tell the complete picture.
Cherry-picking is one form of data distortion, where a writer may communicate favorable data to support their claim and omit the unfavorable information. This practice distorts the reality of data.
To avoid this mistake in your writing, you’ll want to mention who was surveyed in the study to tell the entire story. For example, if 30% of workers love their jobs. You’ll also want to mention the research report surveyed United States citizens in the tech industry in 2019. That way, the reader understands the demographics of the survey participant. Also, it ensures you’re not slicing the data to provide a biased viewpoint.
Correlation vs Causation
Data helps us identify how one variable relates to another variable. We can process data better when we understand the relationship between the data. However, it’s important not to mistake the types of relationships with analyzing the data.
Correlation describes how two variables move in coordination with one another. Correlation can help forecast future trends. On the other hand, causation indicates that one variable is the result of another variable.
Content marketers must be careful not to confuse correlation with causation when presenting stats to their readers. To prevent this mistake, analyze how the data is compared in the research report. Follow these steps:
- Review the stat for a relationship between the data.
- If a relationship exists, consider the type of relationship.
- Do the two variables correlate with one another?
- Is the variable the effect of the other variable?
False equivalence is a logical fallacy where an equivalence is made between two subjects based on flawed or false reasoning. Most people refer to false equivalence as “comparing apples and oranges.”
Develop Good Habits offers a few examples of when equivalence is “false”:
- The argument exaggerates how similar two things are to draw a comparison
- The similar features being focused on are not relevant to the conclusion being drawn
- The argument focuses on similar features of two things while ignoring relevant differences that make them dissimilar
- The argument compares two things that are similar but on completely different orders of magnitude
Content marketers often make the false equivalence mistake to draw irrelevant conclusions to fit a product narrative. For example, you may find a stat that says “44% of SEO professionals use tools to help with their work.” Then, you may imply that SEO pros are using XYZ product.
In this case, you need to give your reader more context about the stat. It’s okay to mention the product, as long as it’s not misleading.
Interpret Statistics to Improve Your Writing Skills
The best content marketers use statistics to educate their readers. However, statistics are only powerful if they’re interpreted accurately. Before you add a stat to your next blog post, find the original source, and review the methodology. Giving proper context to your content will boost your credibility.