What are the most recommended univariate normality tests?

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Does anyone have this article in PDF and could they pass it on to me?

Paper: Wang, A.G., Luo, G.H., 1990. Quantitative relation between the reaction of hydroxylamine and superoxide anion radicals in plants. Plant Physiol. Commun. 6, 55–57.

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Paired t-test or unpaired t-test for my quantitative data with SPSS?

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Chi-square test for allele distribution?

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What kind of t-test can I use if I have two variables measured using the same raw data?

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Ette Etuk

Jarque Bera test and the q-q plot test or normality are quite robust and may be used.

Debopam Ghosh

Anderson- Darling Normality test is one good option, particularly if the sample size is small.

Ankit Nayak

You can use AD test and their p statistics for normality test.

Seyed Mahdi Amir Jahanshahi

As I experienced, the Anderson-Darling and Shapiro-Wilks normality tests are the best ones.

Miguel Patrício

Personally I like the Shapiro-Wilk test: Article Comparing the performance of normality tests with ROC analys...

However, do take the sample size into account. Graphical inspection of the results might actually be the best approach (and never hurts).

Stanislaus S Uyanto

Use Robust Jarque Bera (RJB). The Robust Jarque–Bera (RJB) is the robust version of the Jarque–Bera (JB) test of normality. The Robust Jarque Bera (RJB) is in R Package ‘lawstat’. See the journal article: Uyanto, S. S. An Extensive Comparisons of 50 Univariate Goodness-of-fit Tests for Normality. Austrian Journal of Statistics, 51(3), 45–97. Retrieved from https://ajs.or.at/index.php/ajs/article/view/1279 (an open access, peer-reviewed journal free for everyone to read and download)

Iván Felipe Benavides

All hypotheis-tests are highly sensible to sample size. The higher the sample size, the higher the chance to reject the hypothesis, conclude no to normality and making type-I error.

I recommend you to instead visually check residual behavior from quantile-quantile plots (qq-plots) and residual vs predicted plots.