How to Handle Data That Fail a Normality Test in SPSS

Many students panic when the normality test result in SPSS shows a significance value below 0.05. It feels like the whole analysis stops there. In reality, data that fail a normality test do not automatically mean the research has failed.

What matters is understanding the output, checking the cause, and choosing the right next step. This article focuses on that practical part: not just where to click in SPSS, but how to handle data that turn out to be non-normal.

In SPSS, normality is usually checked through Shapiro-Wilk or Kolmogorov-Smirnov. The common rule is simple:

• Sig. > 0.05 means the data are considered normal,
• Sig. < 0.05 means the data are considered non-normal.

So when people say the normality test “failed,” they usually mean the data do not meet the normality assumption. It is not an SPSS error. The issue is the data distribution or the analytical choice, not that the entire study must be discarded.

A common mistake is looking only at the significance value and immediately concluding that the dataset is problematic. A better approach is to read the result together with visual checks such as a histogram, boxplot, and normal Q-Q plot.

Why? Because statistical tests can be sensitive, especially in certain sample sizes. Even a mild deviation can produce a low significance value. Before making a decision, check whether the distribution is strongly skewed, affected by extreme points, or only slightly off normal.

Data usually fail a normality test for a few common reasons:

• there are outliers or extreme values,
• there is a data entry error,
• the distribution is highly skewed,
• the sample is too small,
• the scale or instrument is not well suited to parametric analysis.

Do not reverse the process. Identify the source of the issue first, then decide how to handle it. If the root problem is unclear, the chosen solution often becomes a patch rather than a sound analytical decision.

One or two extreme values can distort the whole distribution. In SPSS, start with a boxplot and then compare it with the raw data.

If an outlier is caused by input error, correct it. If it is a real observation, do not remove it casually. You need a methodological reason that can be defended in the report. Cleaning data is acceptable, but it must remain transparent and academically justifiable.

Once the cause is clearer, several solutions are commonly used:

1. Fix incorrect data entries. This is the most basic and safest step.
2. Transform the data when appropriate, such as using logarithmic or square-root transformation to reduce skewness.
3. Use a nonparametric test if the data remain non-normal and should not be forced into a parametric approach.

For example, an independent t-test can be replaced with Mann-Whitney, a paired t-test with Wilcoxon, and Pearson correlation with Spearman correlation. The goal is not to make the data look normal at any cost, but to use a method that matches the data.

Many people stop too early when the normality result looks bad. In practice, non-normal data can still be analyzed as long as the method is appropriate. In some situations, a violation of normality is not automatically fatal, especially when the sample size is adequate and there are no extreme outliers.

The main point is this: research does not require artificially perfect data. It requires sound analytical decisions. If you can explain why the data are non-normal and why you chose the next method, your report remains methodologically strong.

If you are unsure how to write the result, a simple statement like this works well:

“Based on the Shapiro-Wilk normality test, the significance value was below 0.05, indicating that the data were not normally distributed. Therefore, the subsequent analysis used an appropriate nonparametric test.”

This wording is clear, honest, and academically safe. There is no need to force a claim of normality when the output shows otherwise.

If you are stuck at the normality-test stage, do not assume your research has failed. Most of the time, what you need is not to restart from zero, but to read the SPSS output correctly and choose the right follow-up method. At Bimbingan Informal, this kind of process can be supported from data checking and output interpretation to selecting the most suitable analysis for a thesis or dissertation.