求助：SPSS中1-Sample K-S检验所有变量Asymp. Sig.均为.000

Hey there! Let’s unpack why you’re seeing all Asymp. Sig. = .000 with the Lilliefors correction (marked by a small c) in your SPSS 1-Sample K-S test, using dataset #18.

First, what do those results mean? #

• The small c next to the p-value tells you SPSS applied the Lilliefors Significance Correction. This kicks in automatically when you test for normality and use sample mean/standard deviation instead of known population values—this adjustment makes the test stricter than the standard Kolmogorov-Smirnov test.
• An Asymp. Sig. = .000 means the test strongly rejects the null hypothesis: your data does not come from the distribution you’re testing (almost certainly the default normal distribution).

What you might have missed (and how to fix it) #

Here are the most likely reasons all variables are showing this result:

• You’re testing for normality, but dataset #18 isn’t normally distributed
  Many teaching datasets are intentionally non-normal (e.g., strongly skewed, discrete, or packed with extreme values). Before relying on the K-S test, visualize your data:
  • Plot histograms, boxplots, or kernel density plots for each variable to see their actual distribution shape.
  • Calculate descriptive stats like skewness and kurtosis—values far from 0 (skewness > |2|, kurtosis > |7|, for example) are clear red flags for non-normality.
• You didn’t adjust the test distribution
  The 1-Sample K-S test defaults to testing normality, but if dataset #18 is supposed to follow a different distribution (e.g., uniform, Poisson, exponential), you need to manually select it in the SPSS dialog:

  In the 1-Sample K-S window, under Test Distribution, pick the correct distribution instead of leaving it on the default Normal.

• Sample size is too large
  With very large samples, even tiny, practically irrelevant deviations from the target distribution will trigger a tiny p-value (like .000). Statistical significance doesn’t always equal meaningful deviation here—focus on visualizations and effect sizes instead of just the p-value.
• Missing data issues
  Double-check if missing values are being handled correctly. SPSS defaults to listwise deletion, but if this leaves you with an unusual sample (e.g., a subset that’s even more non-normal), it could skew results. Verify your dataset’s missing value flags in the Data View.

Next steps to diagnose #

1. Visualize every variable to confirm their distribution.
2. Confirm you’re testing the correct distribution (not just default normality).
3. Compare descriptive stats to the target distribution’s properties.

内容的提问来源于stack exchange，提问作者Spasoje Petronijević