Update 2-4 Local Performance Testing.md

chapters/2-Measuring-Performance/2-4 Local Performance Testing.md

@@ -23,7 +23,7 @@ Let's describe the terms indicated on the image:
 
 By looking at the box plot in Figure @fig:BoxPlot, we can sense that our code change has a positive impact on performance since "after" samples are generally faster than "before". However, there are some "before" measurements that are faster than "after". Box plots allow comparisons of multiple distributions on the same chart. The benefits of using box plots for visualizing performance distributions are described in a blog post by Stefan Marr.[^13]
 
-Performance speedups can be calculated by taking a ratio between the two means. In some cases, you can use other metrics to calculate speedups, including median, min, and 95th percentile, depending on which one is more representative for your distribution.
+Performance speedups can be calculated by taking a ratio between the two means. In some cases, you can use other metrics to calculate speedups, including median, min, and 95th percentile, depending on which one is more representative of your distribution.
 
 *Standard deviation* quantifies how much the values in a dataset deviate from the mean on average. A low standard deviation indicates that the data points are close to the mean, while a high standard deviation indicates that they are spread out over a wider range. Unless distributions have low standard deviation, do not calculate speedups. If the standard deviation in the measurements is on the same order of magnitude as the mean, the average is not a representative metric. Consider taking steps to reduce noise in your measurements. If that is not possible, present your results as a combination of the key metrics such as mean, median, standard deviation, percentiles, min, max, etc.