What is a Statistical Significance? Definition, Examples & Best Practices
Statistical Significance is a term used in survey research and market analysis. Understanding statistical significance helps researchers design better surveys, collect higher-quality data, and draw more reliable conclusions. It is relevant across customer experience, employee research, brand tracking, and academic studies.
Statistical Significance Definition
Statistical Significance is a concept that plays an important role in survey methodology and market research. The primary keyword for this term is 'statistical significance'.
Researchers and practitioners use statistical significance when designing studies, collecting responses, or interpreting data. A solid understanding of statistical significance helps avoid common errors and improves the reliability of research findings.
Secondary search queries for this topic include: what is statistical significance, p value explained, significance testing survey. Each reflects a slightly different angle on the same core concept.
Why Statistical Significance Matters
Getting statistical significance right has a direct impact on data quality. When researchers misapply or overlook this concept, it often shows up later as unexplained variance in results, lower response rates, or findings that cannot be confidently acted on.
According to Dillman, Smyth, and Christian in Internet, Phone, Mail, and Mixed-Mode Surveys (4th ed., 2014), many of the most persistent sources of error in survey research trace back to decisions that touch on concepts like statistical significance.
How Statistical Significance Is Used in Practice
In customer experience programmes, statistical significance informs how surveys are structured and distributed. In employee research, it shapes question design and result interpretation. In brand tracking and market research, it determines how data is collected and compared over time.
MindProbe supports researchers working with statistical significance through purpose-built features in the survey builder and analytics dashboard, reducing the manual work involved in applying best practices.
Common Mistakes Related to Statistical Significance
The most common errors involving statistical significance in survey research tend to fall into three categories: misidentifying when the concept applies, applying it inconsistently across a study, and failing to account for it when interpreting results.
Piloting surveys before full distribution and checking results against known benchmarks are the two most reliable ways to catch problems before they affect your data.
Statistical Significance and Survey Quality
Survey quality depends on consistent application of methodological principles, and statistical significance is one of those principles. Whether you are running a one-off customer feedback study or a continuous brand tracking programme, understanding and correctly applying this concept improves the reliability of what you collect.
Frequently Asked Questions
Statistical Significance is a concept in survey research that relates to statistical significance. It is used by researchers, CX professionals, and market analysts to improve data quality and ensure survey results are reliable and actionable. The key is understanding when and how it applies to your specific research context.
Statistical Significance affects survey results by influencing how respondents engage with questions and how the resulting data can be interpreted. Applied correctly, it improves measurement accuracy. Overlooked, it can introduce systematic errors that skew findings in ways that are hard to detect after data collection has closed.
The decision to focus on statistical significance depends on your research objective and the population you are studying. It is most relevant when designing surveys for scale, when comparing results across groups or time periods, or when the accuracy of measurement is critical to a business or academic decision.
Common mistakes include misapplying the concept to unsuitable research designs, using inconsistent definitions across survey waves, and failing to account for statistical significance when interpreting results. Piloting surveys and reviewing methodology with a second set of eyes before launch catches most of these errors.
Good starting points include Dillman, Smyth, and Christian's Internet, Phone, Mail, and Mixed-Mode Surveys, Pew Research Center's methodology documentation, and AAPOR's best practice standards. For practical application, MindProbe's help documentation covers how the platform's features relate to concepts including statistical significance.