Why Your SPSS Result Is Significant but Your Hypothesis Still Isn’t Supported

You run the test. The Sig. column says 0.024. Brilliant, you think, until your eyes drift left and land on the correlation coefficient: -0.30.

Your hypothesis predicted a positive relationship. SPSS has just handed you a negative one.

So, is your hypothesis supported? No, it isn’t. But what you do next matters more than the answer, and it’s where dissertation marks are quietly won or lost. Let’s work through it properly.

Significant does not mean supported

SPSS tests your data against the null hypothesis, which says there is no effect, difference or relationship. It has no idea what you predicted. It simply reports how unusual your data would look if nothing were going on.

Take a psychology student studying sleep and exam marks. She expects longer sleepers to score higher. Her Pearson correlation gives r = -0.30, p = 0.024.

An association shows up in her sample. It just runs the opposite way to her prediction.

The p-value tells you how strongly the data challenge the null hypothesis. The coefficient tells you the direction and estimated strength of the relationship.

Where people go wrong

The same three slips appear again and again in quantitative write-ups.

Reading the p-value and nothing else. Check the sign of r, or the direction of the group means, before anything else.

Confusing significance with strength. A p of 0.003 alongside r = 0.11 is perfectly possible. The result is statistically significant, but the relationship itself is weak.

Treating non-significance as proof of nothing. A small sample can hide a real effect. “No evidence found” is not the same as “no effect exists.”

An unexpected result is not a failed project. Often it just means the original prediction was incomplete.

The four ideas worth getting straight

If you’re searching for Professional SPSS Assignment Help UK, you’ll find that good support always returns to these four ideas, because they sit underneath every output table.

The null and alternative hypotheses. H₀ says there is no relationship. H₁ says there is one, and if you wrote it with a direction, that direction is part of what you’re testing.

The p-value. It shows how surprising your result would be if the null were true. It is not the probability that the null is true, and 0.024 does not mean a 97.6% chance you’re right.

Effect size. Pearson’s r and Cohen’s d tell you how big the effect is. Large samples can make tiny effects significant.

Confidence intervals. A mean difference of 3.2 with a 95% interval from 0.7 to 5.7 is positive, but the true size could be small or fairly large. The wider the interval, the less precise the estimate.

One small detail: Sig. = .000 does not mean zero. Report it as p < .001.

The one-tailed trap most students miss

This rarely gets explained properly. SPSS gives a two-tailed p-value by default. A one-tailed test halves the p-value when the result falls in the predicted direction, which is exactly why it is so easy to misuse.

If the result lands the wrong way, the one-tailed p-value is not small. It is roughly 1 minus half the two-tailed value. In the sleep example, a two-tailed p of 0.024 becomes a one-tailed p of about 0.99 for a positive hypothesis. The data point firmly against the prediction.

A one-tailed test must be justified before you examine the results, never chosen afterwards because it gives a more favourable p-value.

A directional hypothesis alone does not automatically justify it either. You need a defensible methodological reason, such as a theory or earlier evidence that makes an effect in the opposite direction meaningless or impossible. State that reason in your methodology chapter. If you can’t, stay with two-tailed.

A four-look check for any output

Use this order every time. It keeps your research question in charge.

Here’s a worked example. The hypothesis: weekly working hours are positively associated with academic performance. The output: r = -0.34, p = 0.012, N = 54.

Look 1: Direction. r is negative, so more working hours go with lower marks in this sample.

Look 2: Evidence. p = 0.012 is below 0.05, so the association is statistically significant, assuming the test’s assumptions hold.

Look 3: Size. The squared correlation is approximately 0.12, meaning the two variables share around 12% of their variance in this sample. Real, but modest.

Look 4: Sanity. Check the descriptives, the coding and any outliers. A scale where 1 means “strongly agree”, entered the wrong way round, can flip a correlation’s sign entirely. Coding errors are far more common than genuinely surprising findings.

Only then write it up:

“A Pearson correlation found a statistically significant negative association between weekly working hours and academic performance (r = -0.34, p = 0.012, N = 54). The variables shared approximately 12% of their variance, and the direction did not support the predicted positive relationship.”

Explanations such as less time for independent study are plausible, but a correlation cannot confirm them. Present them as ideas for further research.

Seven reporting habits that cost marks

Calling a hypothesis proven. Significance is evidence against the null, not proof of your idea.

Dropping the sign. Always say whether the relationship is positive or negative.

Rewriting the hypothesis afterwards. Keep the original prediction and describe the new pattern as exploratory.

Treating 0.049 and 0.051 as different worlds. Report exact values and judge them in context.

Claiming causation from correlation. Other variables may be doing the work.

Ignoring multiple tests. Run enough correlations and some will look significant by chance. Consider an adjustment such as Bonferroni.

Losing the paper trail. Record your hypothesis, test, statistics and interpretation so your methods, results and discussion chapters agree.

What to take away

An unexpected result is rarely the end of your analysis. It is usually the start of a better discussion section.

Report what you found, plainly. Say whether the prediction held. Then explore why: perhaps the sample, the measure, or a variable you never considered.

One practical tip: SPSS doesn’t give a confidence interval for Pearson’s r by default, but the Bootstrap button in the Bivariate Correlations dialog will add one, provided your licence includes the bootstrapping option. Including it shows you understand precision, not just significance.

An unexpected finding is not a weakness in itself. A clear account of what the results show, where they depart from your prediction and what limitations remain is more convincing than an interpretation bent to fit.

So before you panic over an output that disagrees with you, run the four looks: direction, evidence, size, sanity.

Good research isn’t about getting the answer you hoped for. It’s about understanding the answer your evidence actually gives you.