Understanding statistical significance: A comprehensive guide with examples
Understand the basics of statistical significance. Learn the definition and formula and see examples to help you analyse data and make informed decisions.
What’s the very first step after conducting research? Proving that your results have statistical significance. But what exactly does that mean and how can a business use statistical significance to enhance its data analysis?
In this article, we’ll define statistical significance, demonstrate how to prove it by giving some examples and discuss how you can improve your level of data significance.
What is statistical significance?
Statistical significance is the certainty that an outcome is due to a specific cause rather than chance. If a result observed in data is statistically significant, this would imply that there is a high probability that a specific factor or circumstance has caused the outcome. Conversely, low statistical significance would be when it is likely that the result could be attributed to chance.
For example, let’s suppose that a market researcher compares two advertising slogans, with 200 people seeing slogan A and 200 seeing slogan B. While 55% remember A, only 45% remember B. To check whether this difference is meaningful, she uses a statistical test, i.e. a mathematical method that calculates the probability of getting these results by chance.
If the test shows that the difference is statistically significant, then it’s likely that the 10% gap is due to slogan A being more memorable rather than due to random variation. This helps the company choose the more effective slogan for their campaign.
Statistical significance vs. statistical difference
Although they are related, statistical difference and statistical significance are distinct concepts. A statistical difference relates to a statistically significant difference between two data sets.
For example, let’s suppose that a company is surveying unhappy customers. They could notice a statistical difference in the total number of women who disliked the product compared to men who did. A statistically significant difference would mean a large difference between the two groups, large enough not just to be random or caused by chance.
Although all statistically significant results show a statistical difference, not all statistical differences are statistically significant. The key is determining whether the observed difference is meaningful or just noise in the data.
Statistical significance vs. margin of error vs. confidence level
There are three main terms that tend to confuse researchers when working with statistical analysis. Let’s discuss what each of these terms means and note their differences:
- Statistical significance: The likelihood of something happening due to weighty factors rather than chance.
- Margin of error: The range of errors in your survey results caused by randomness. This is the degree to which a deviation from your target is appropriate.
- Confidence level: A reflection of how certain you are that your data is accurate and reflects the thoughts or opinions of your customer base.
All three of these concepts are useful in statistical analysis. Margin of error and confidence level work in tandem to outline how accurate your results are. Statistical significance, on the other hand, demonstrates whether or not your results are meaningful or simply just random.
How to calculate statistical significance
If your business would like to rapidly calculate statistical significance, try the SurveyMonkey statistical significance calculator.
Alternatively, here are the steps that you should follow when calculating statistical significance manually:
- Decide what to test: Identify the result or metric that you will calculate and outline the data that you’ll need to collect.
- State your hypothesis: Outline the outcome you expect from the experiment. You could use a null hypothesis, “There is no difference in engagement between these two email campaigns”, or an alternative hypothesis, “Email campaign A has a higher engagement rate than campaign B.”
- Collect data: Leverage surveys to gather data about your chosen subject. The larger your data set, the higher your degree of confidence in your final outcomes.
- Calculate your expected results: Based on your initial hypothesis, run a calculation with your expected figures to provide a benchmark.
- Compare expected results to your actual results: Once you’ve collected a large volume of data, you can calculate your results. You can see how these figures differ by comparing the actual and expected results.
- Determine your sum: Use a statistical analysis test to determine the probability of obtaining your results. You will need to calculate the statistical significance of your data. Use the statistical significance formula below for this step.
- Report your results: Finally, based on your observations and your level of confidence in their accuracy, you can report your results to your organisation. Creating a story from your data will help to show stakeholders why your research is important and justify any actions you want.
Statistical significance formula
When manually calculating statistical significance, you can use the formula for statistical significance. This calculation will allow you to determine whether the difference between two data sets is due to the influence of certain factors or simply down to chance.
The formula for statistical significance is as follows:
- χ2=Σ(E(O−E)2)
In this equation:
- Σ (Sigma) = The sum of the total terms.
- O = The observed values that you have recorded in your data.
- E = The expected values you input when outlining your experiment.
By gathering these pieces of data and inputting them into the statistical significance formula, you can definitively state whether or not your data is significant.
How to understand statistical significance for survey results
Now that we understand how to calculate statistical significance, it’s time to discover why this is important.
Here are a few different examples of what is considered statistically significant:
- Marketing example: Businesses can use marketing surveys to assess customer interest in a new product or feature. If 85% of your customers respond positively to the integration of a new feature, then there is a high degree of confidence and little doubt about statistical significance.
- A/B test example: A/B testing allows marketing teams to determine which of two approaches is more effective. For example, teams could use statistical significance to prove that one landing page has higher engagement than another. The A/B testing calculator demonstrates this, with higher engagement on certain variants being above the threshold for statistical significance.
Investor market analysis example: Working with investment professionals, SurveyMonkey helped investors better understand financial market sentiments. By providing statistical significance in your research, you, too, can demonstrate how market sentiment has an impact on the market, particularly in relation to how people expect markets to perform.
Contextualising your statistical significance test
Remember that there are more pieces to the puzzle than just statistical significance. You’ll have to conduct further research into your data to determine whether your results are meaningful and how confident you are in their accuracy. After all, if your sample size is only 10 people, then even statistical significance may not prove the accuracy and utility of your data.
Depending on your margin of error and confidence level, there could be more to the story. If you’d like to streamline statistical analysis, use SurveyMonkey to rapidly reveal your statistical significance and calculate the meaning behind your data.
Tips for increasing your level of statistical significance
Generating data with statistical significance is, ironically, not random. You can employ several strategies to enhance the quality of your data and improve your ability to improve statistical significance.
Improve your survey response quality
Cleaning your data before conducting analysis will help to remove outliers, decrease the presence of anomalies and enhance your confidence level in the data.
Here is how you can enhance the quality of your survey responses:
- Use machine learning (ML): ML tools like Response Quality will scan open- and closed-ended questions, flagging any that seem like poor-quality responses.
- Use screening questions: A few initial screening questions will make sure that only the relevant target of your survey ends up filling it in.
- Eliminate anomalies: There will be major outliers in every data set. Typically, you should remove significant anomalies as they could be indicative of response mistakes, data entry errors or ineffective data capture.
Get survey responses from your target market
Another method of increasing your statistical significance level is to ensure that your data is of the highest quality possible. Answers from people who don’t understand the topic or cannot give honest feedback about what you’re asking will skew your results and lead to lower statistical significance in your data.
To avoid this, send your surveys to the right audience segments. If you’re testing a new product, you should only survey people who have had experience with it. Segment your audience where possible and send your surveys to these unique segments.
If you don’t currently have access to a wide enough audience to provide accurate and high-confidence data, you could always leverage SurveyMonkey Audience. With Audience, you can survey millions of verified users across the globe, getting instant feedback from the exact audience segments that you need.
Related reading: 7 examples of how to make better decisions with your own data.
Use survey best practices for reliable results
The data analysis that you conduct is only ever as good as the data that you can collect. To collect better data, you need to be asking the right questions.
Here are a few leading strategies that you should be using when writing your surveys:
- Utilise good survey design: Small additions, such as adding a total number of questions to the survey, will help to increase completion rates and ensure that people don’t get bored with your survey and rush through it.
- Keep questions short: Any ambiguity, whether due to poor wording or being too long, can confuse your readers and reduce the quality of the responses that they give.
- Use pre-written templates: Survey templates allow your business to leverage expert-written surveys. With these, you’ll be able to access high-quality results with ease.
Related reading: 10 tips to improve your online surveys.
Identify meaningful results with SurveyMonkey
You need to conduct statistical analysis to get to the heart of your data. Calculating statistical significance is a major part of this, helping to guide you towards meaningful results. It’s always a good idea to be certain, especially when managing any form of data collection that could have results affecting the direction that your business takes.
With SurveyMonkey, your business can access leading tools, expert-built templates and other solutions as well as a powerful survey panel that will provide reliable, high-quality results.
Collate, analyse and draw meaning from your data faster with SurveyMonkey. Get started today to integrate meaningful results into your business.