If the p-value for the AD test is greater than 0.05, then you can also assume normality, BUT keep in mind that for very large sample sizes the AD test can get picky about normality and tend to say even fairly normally distributed data are not normal. If the data points fall roughly on a straight line, you can assume the data are normal. For most situations this is likely easiest and more intuitive done using a probablity plot (Stat > Basic Statistics > Normality test), but you can also use the Anderson-Darling normality test. It is critial that you assess the normality of the data. Dieter brought up a very important point.
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