When performing a rank correlation test, one alternative to using the Critical Values of Spearman's Rank Correlation Coefficient table to find critical values is to compute them using this approximation:
$$r _ { S } = \pm \sqrt { \frac { t ^ { 2 } } { t ^ { 2 } + n - 2 } }$$
where $t$ is the $t$-score from the $t$ Distribution table corresponding to $n - 2$ degrees of freedom. Use this approximation to find critical values of $r _ { \mathrm { S } }$ for the case where $n = 40$ and $\alpha = 0.10$.
A) $\pm 0.312$
B) $\pm 0.304$
C) $\pm 0.264$
D) $\pm 0.202$

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The Answer of When performing a rank correlation test, one alternative to using the Critical Values of Spearman's Rank Correlation Coefficient table to find critical values is to compute them using this approximation:
$$r _ { S } = \pm \sqrt { \frac { t ^ { 2 } } { t ^ { 2 } + n - 2 } }$$
where $t$ is the $t$-score from the $t$ Distribution table corresponding to $n - 2$ degrees of freedom. Use this approximation to find critical values of $r _ { \mathrm { S } }$ for the case where $n = 40$ and $\alpha = 0.10$.
A) $\pm 0.312$
B) $\pm 0.304$
C) $\pm 0.264$
D) $\pm 0.202$

When Performing a Rank Correlation Test, One Alternative to Using rS=±t2t2+n−2r _ { S } = \pm \sqrt { \frac { t ^ { 2 } } { t ^ { 2 } + n - 2 } }rS​=±t2+n−2t2​​

When Performing a Rank Correlation Test, One Alternative to Using $r _ { S } = \pm \sqrt { \frac { t ^ { 2 } } { t ^ { 2 } + n - 2 } }$