Update check-power.qmd

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tutorial_pages/check-power.qmd

@@ -27,7 +27,7 @@ The sample size `n` we need given this set of parameters, is `64` per group.
 
 Just as we can check the alpha of our test by sampling from the same distribution (i.e. simulating data without an effect), we can check the power by sampling from different distributions (i.e. simulating data with an effect).
 
-If we sample values from two normal ditributions with different means (e.g. N(0,1) and N(0.5,1)), what is the minimum sample size we need to detect a significant difference in means with a t.test, 80% of the time?
+If we sample values from two normal ditributions with different means (e.g. N(0,1) and N(0.5,1)), what is the minimum sample size we need to detect a significant difference in means with a t-test, 80% of the time?
 
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@@ -58,9 +58,11 @@ Using simulations for power analysis is not really necessary for simple examples
 When analyses become complex and it is hard or impossible to determine a sample size analytically (i.e. you can't calculate it, or there's no suitable function to use), then simulations are an indispensible tool.
 
 A simple example of a power analysis like the one you've just done can be found in the "Power analysis" section of this paper:
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 * Blanco, David, et al (2020). "Effect of an editorial intervention to improve the completeness of reporting of randomised trials: a randomised controlled trial." BMJ open 10.5: e036799. <a href="https://doi.org/10.1136/bmjopen-2020-036799" target="_blank">https://doi.org/10.1136/bmjopen-2020-036799</a>
 
 A complete self-paced tutorial to simulate data for power analysis of complex statistical designs can be found here:
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 * <a href="https://lmu-osc.github.io/Simulations-for-Advanced-Power-Analyses/" target="_blank">https://lmu-osc.github.io/Simulations-for-Advanced-Power-Analyses/</a> 
 
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