Question about running Spearman's correlation instead of Pearson's I have a question about correlations. Let's say I want to measure the associations between a number of variables and all of them are measured on a ratio scale. I know I can use Pearson's $r$ correlation to do this, assuming the assumptions to run Pearson's correlation are met (i.e., normality, linearity, no outliers, homoscedasticity, etc.).
However, let's say I just wanted to save time and not have to check all of these assumptions and instead calculate Spearman's $\rho$ correlations (as there are far fewer assumptions to check – all one really has to check for is that relationships are monotonic).
This of course begs the question: If I do this, will it in any way give less precise/less accurate results? I've heard that this is something that isn't a big deal, but I thought I'd ask.

Pearson's correlation coefficient ($\boldsymbol{r}$) provides a measure of linear association between paired variables.
Spearman's correlation coefficient ($\boldsymbol{r_{\bf{S}}}$) provides a measure of monotonic association between paired variables.
The latter is different than the former. A linear relationship is just one kind of monotonic relationship between two variables. For example, an exponential curve, a logistic curve, and a stair step function are also all examples of monotonic functions between $x$ and $y$. Because of this, it is possible to observe $r > r_{\text{S}}$, $r \approx r_{\text{S}}$ and $r < r_{\text{S}}$ to a significant degree depending on what's going on in your data. These two measures can even have different signs.
As a consequence, without knowing the specifics of your data and sample distributions ahead of time, it is probably best to respect that $\boldsymbol{r}$ and $\boldsymbol{r_{\bf{S}}}$ are measures of substantively different things.
Aside: Spearman's correlation can also be thought of as a measure of linear association between ranks of paired variables..
