CaptainBlack said: Lets hope this is right this time Under the null hypothesis $x_n$ is the sum of $7$ independent identically distributed RVs $Y=(X-5)^2$ with mean $$ \mu_Y=\overline{(X-5)^2}=\sigma^2=1 $$ and variance $$ v_Y=E((Y-\mu_Y)^2)=E(Y^2-2\mu_Y Y+\mu_Y^2)=m_4(X)-2 \mu_Y^2+\mu_Y^2 $$ where $m_4(X)$ is the fourth central moment of $X$ and as $X$ has a normal distribution is equal to $3\sigma^4$, so: $$ v_Y=3-2+1=2 $$ Now because $x_n$ is the sum of seven iid RVs with means $\mu_Y=1$ and variance $v_Y=2$ we know from the CLT that $x_n$ is approximatle $\sim N(7\mu_Y, 7v_Y)$ Numerical/Monte-Carlo check on this: Code: x=randn(1000000,7)+5;xx=sum((x'-5).^2)';mean(xx),var(xx) ans = 6.9945 ans = 14.004 Click to expand... Thank you! Could you please advise me some steps to solving it?