Many students reach the results section, see a p value less than 0.05, and assume everything is correct. Others get confused when results are not significant and do not know what to do next. This is one of the most misunderstood parts of statistical analysis.
A p value less than 0.05 simply means that the result is statistically significant under conventional thresholds. It suggests that the observed relationship or difference is unlikely to have occurred by chance. However, this is where many dissertations go wrong. Statistical significance alone does not validate your model, your data, or your conclusions.
If your dataset has issues from the beginning, even a “significant” result can be misleading. This is why proper preparation, including structured SPSS data entry and careful data cleaning in SPSS, is critical before interpreting any p value.
Why a p Value Less Than 0.05 Does Not Mean Your Analysis Is Correct
This is where most students lose marks. A significant p value does not mean:
- Your model is correctly specified
- Your assumptions are satisfied
- Your variables are properly measured
- Your interpretation is valid
Supervisors often reject results even when p < 0.05 because the underlying analysis is flawed. For example, running regression without checking assumptions or using the wrong statistical test will still produce a p value, but the result cannot be trusted.
If your regression model is involved, it is important to understand how results should be interpreted correctly, as explained in this guide on multiple linear regression in SPSS.
What to Do When You Get p < 0.05 but Results Do Not Make Sense
This situation is more common than most students expect. You may find that:
- Coefficients contradict your hypothesis
- The effect size is very small
- The direction of relationships is unclear
In such cases, the issue is not the p value itself. The problem usually comes from:
- Poor variable selection
- Multicollinearity
- Incorrect model choice
- Data inconsistencies
Before interpreting results, revisit your dataset and confirm everything is structured correctly. Many of these issues fall under common SPSS errors that can silently distort results.
When p Value Less Than 0.05 Actually Matters
A significant result becomes meaningful only when combined with:
- A well-justified model
- Proper assumption testing
- Clear theoretical backing
- Logical interpretation
For example, in correlation analysis, a significant p value should align with a meaningful relationship between variables. Understanding how correlation works in SPSS is essential, especially when working with methods like Pearson correlation in SPSS.
Without this context, reporting p < 0.05 becomes a mechanical exercise rather than a meaningful analysis.
What If Your p Value Is Greater Than 0.05?
Many students panic when their results are not significant. However, a non-significant result does not mean failure. It may indicate:
- No strong relationship exists
- The sample size is too small
- Measurement tools are weak
Understanding how to handle non-significant results is just as important. If you are facing this issue, it is worth reviewing how to interpret such outcomes properly, as explained in p value greater than 0.05.
The Real Problem: Interpretation in Dissertation Writing
The biggest challenge is not obtaining a p value. It is explaining it correctly in your dissertation.
Many students:
- Report p values without context
- Ignore effect sizes
- Fail to connect results to research questions
- Misinterpret statistical significance as practical importance
This is why even technically correct analyses get rejected. If your supervisor has flagged your results or asked for revisions, the issue is likely interpretation, not calculation. This is a common frustration, especially when guidance is limited, as seen in cases discussed under dissertation supervisor not helping.
Choosing the Right Statistical Test Matters More Than the p Value
A p value is only as valid as the test used to generate it. Using the wrong test leads to misleading conclusions.
For example:
- Using ANOVA when multiple dependent variables exist may be incorrect
- In such cases, approaches discussed in MANOVA vs ANOVA become relevant
Similarly, applying regression without understanding assumptions can invalidate results, even if p < 0.05.
When Your Results Keep Getting Rejected
If you have:
- Re-run your analysis multiple times
- Obtained significant results but still feel unsure
- Received feedback that your interpretation is unclear
then the issue is deeper than the p value.
At this stage, working with structured support such as dissertation data analysis services helps ensure your results are not only statistically correct but also academically acceptable.
FAQs
What does p value less than 0.05 mean in simple terms?
It means the result is statistically significant and unlikely to have occurred by chance.
Is p < 0.05 always good in a dissertation?
No. It must be supported by correct methodology and interpretation.
Can p < 0.05 be wrong?
Yes. If your data or model is flawed, the result can be misleading.
Why did my supervisor reject my results despite significance?
Likely due to poor interpretation, missing assumptions, or incorrect model choice.
What should I report alongside p values?
Effect size, confidence intervals, and practical interpretation.
What if my results contradict my hypothesis?
You should report them objectively and explain possible reasons.
Does p < 0.05 prove causation?
No. It only indicates statistical significance, not causality.
Can small samples affect p values?
Yes. Small samples reduce statistical power.
What causes misleading p values?
Data errors, wrong tests, and assumption violations.
Should I rely only on p values?
No. Always consider the full statistical context.
What if my SPSS output is confusing?
You may need to review issues related to SPSS output not showing or interpretation errors.
How do I know if I used the right test?
It depends on your variables and research design. Resources like statistical tools in research can guide selection.
Can poor data entry affect significance?
Yes. Errors during SPSS data entry can distort results.
What if my results are inconsistent?
Check for issues like multicollinearity, outliers, and data quality problems.
When should I seek help with analysis?
When results do not make sense, interpretation is unclear, or deadlines are approaching.
