Statistical Significance vs. Meaningfulness in Research
This paper examines the distinction between statistical significance and meaningfulness in research contexts, with particular attention to exploratory studies. The author argues that lowering significance thresholds — such as relaxing the p-value from .05 to .10 — to achieve a "statistically significant" label undermines research credibility rather than strengthening it. Drawing on the American Statistical Association's 2016 statement on p-values, the paper contends that meaningful research depends on validity, reliability, appropriate methodology, and the researcher's ability to explain real-world relevance — not on whether findings clear an arbitrary significance cutoff.
- Statistical Significance and Meaningfulness Defined: Defines meaningfulness versus statistical significance in research
- The Problem with Relaxing Significance Thresholds: Critiques lowering p-value thresholds in exploratory studies
- Exploratory Research and the Role of Findings: Explains how exploratory findings have value without significance
- Researcher Bias and Credibility: Warns against bias undermining research standards and labels
- Reporting Results with Validity and Trustworthiness: Argues researchers must report findings honestly and clearly
✍️ How to write this paper — guide, tools & examples ▾
What makes this paper effective
- Uses a memorable analogy — comparing relaxed significance standards to mislabeling products as "organic" — to make an abstract methodological point concrete and accessible.
- Grounds its argument in an authoritative external source (the American Statistical Association's 2016 statement), lending credibility to a position that might otherwise seem opinion-based.
- Maintains a clear, consistent throughline: meaningfulness and credibility matter more than achieving a significance label, repeated and reinforced across each section.
Key academic technique demonstrated
The paper demonstrates effective use of analogy as a rhetorical and explanatory device. By comparing lowered significance thresholds to loosening "organic" labeling standards, the author translates a technical statistical concept into intuitive terms. This technique is especially useful in applied research methods writing, where readers may grasp logical consequences more readily through familiar real-world scenarios than through abstract statistical reasoning alone.
Structure breakdown
The paper opens by defining meaningfulness in contrast to statistical significance, then narrows to critique the specific practice of relaxing p-value thresholds in exploratory research. It draws on the ASA's statement to anchor the argument, extends the discussion to what should actually validate exploratory findings, warns against researcher bias, and closes by emphasizing the researcher's obligation to report results honestly and explain their real-world relevance.
Statistical Significance and Meaningfulness Defined
Meaningfulness refers to the practical, real-world application of a statistic. If a statistically significant correlation between variables has meaningfulness, that correlation says something to the real world, and understanding it can have an impact on how people adjust their behavior going forward. Therefore, while statistical significance is helpful, it is not the end-all-be-all of research: a research finding must be meaningful for it to have importance. It must have some sort of impact in the real world to qualify as meaningful.
For researchers who stated that "given this research was exploratory in nature, traditional levels of significance to reject the null hypotheses were relaxed to the .10 level," one important observation is that statistical significance is not something that necessarily has to be considered of paramount importance in exploratory research. Statistical significance is more appropriate for testing a hypothesis. For an exploratory research study, the researcher can get by with simply identifying possible connections.
The Problem with Relaxing Significance Thresholds
Moreover, if the levels of statistical significance have to be lowered from their standard or traditional thresholds in order to convey significance, one should seriously consider whether the significance is genuinely present. Beyond that, the central question is whether the relationship is meaningful. Predictor and response variables can be helpful in identifying patterns of behavior for a given subject. However, it does little good to lower significance thresholds simply to apply the "statistically significant" label to findings — much like selling a can of peas to GMO-averse consumers by labeling it "organic" when it does not meet that standard.
The researcher of an exploratory study should focus more on explaining why the relationship is meaningful and less on worrying about statistical significance. Instead of moving the threshold, the researcher should examine why the findings did not express significance according to traditional levels. Questions worth asking include: Was the sample too small? Was the test appropriate? There could be many different reasons for the finding.
As the American Statistical Association (2016) points out, "a p-value, or statistical significance, does not measure the size of an effect or the importance of a result." In other words, a p-value only speaks to the strength of the relationship between variables — it is not an indicator of meaningfulness. In order for meaningfulness to be explained, the researcher must look at method, research design, the problem the research is supposed to address, context, sample, theoretical application, and related considerations. The researcher must be able to explain why a relationship is meaningful or why it applies to the real-world problem at hand.
References
American Statistical Association. (2016). American Statistical Association releases statement on statistical significance and P-values. Retrieved from
Create your account
Always verify citation format against your institution’s current style guide requirements.