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If the SW test shows significant results with such a small sample size and also the QQ plot say not normal, so ... not normal

But with such a small sample size even one outlier may change to non-Normal data.

So if the data looks valid you may say it doesn't distribute normally.

Usually, a normal distribution is not a target by itself, but you need it to be able to use tools that are based on the normal distribution.

For example you may use this data for t-test ...despite your results since it is reasonably symmetrical and you run the test on the average (CLT)

Hello everyone, I am facing some problems concerning my data normality of my dependent variable,

Pearson correlation) normality of the dependent variable is irrelevant. It is the

distribution of the residuals (prediction errors) which might be important in some

analyses, or in case of the t test the distribution within each group. But usually only if

sample size is small. If sample size is large enough (n > 30 or so) most analyses are

robust against non-normal residuals. You should have answered obh's question for

which actual purpose you need to know whether the variables are distributed normally

in the population.

With kind regards

Karabiner

If the SW test shows significant results with such a small sample size and also the QQ plot say not normal, so ... not normal

But with such a small sample size even one outlier may change to non-Normal data.

So if the data looks valid you may say it doesn't distribute normally.

Usually, a normal distribution is not a target by itself, but you need it to be able to use tools that are based on the normal distribution.

For example you may use this data for t-test ...despite your results since it is reasonably symmetrical and you run the test on the average (CLT)

Why do you care? In almost every statistical analysis (except the statistical test for the

Pearson correlation) normality of the dependent variable is irrelevant. It is the

distribution of the residuals (prediction errors) which might be important in some

analyses, or in case of the t test the distribution within each group. But usually only if

sample size is small. If sample size is large enough (n > 30 or so) most analyses are

robust against non-normal residuals. You should have answered obh's question for

which actual purpose you need to know whether the variables are distributed normally

in the population.

With kind regards

Karabiner

Pearson correlation) normality of the dependent variable is irrelevant. It is the

distribution of the residuals (prediction errors) which might be important in some

analyses, or in case of the t test the distribution within each group. But usually only if

sample size is small. If sample size is large enough (n > 30 or so) most analyses are

robust against non-normal residuals. You should have answered obh's question for

which actual purpose you need to know whether the variables are distributed normally

in the population.

With kind regards

Karabiner

Thank you so much sir I really appreciate it

Pearson correlation) normality of the dependent variable is irrelevant. It is the

distribution of the residuals (prediction errors) which might be important in some

analyses, or in case of the t test the distribution within each group. But usually only if

sample size is small. If sample size is large enough (n > 30 or so) most analyses are

robust against non-normal residuals. You should have answered obh's question for

which actual purpose you need to know whether the variables are distributed normally

in the population.

With kind regards

Karabiner

With such a small sample size, have you perhaps considered using Spearman's rho instead

of Pearson. It does not represent linear relationships but rather monmotonous relationships,

but it is independent of distributional assumptions.

With kind regards

Karabiner

of Pearson. It does not represent linear relationships but rather monmotonous relationships,

but it is independent of distributional assumptions.

With kind regards

Karabiner