diff --git a/+Simulator/@TwoDimensionalMOT/bootstrapErrorEstimation.m b/+Simulator/@TwoDimensionalMOT/bootstrapErrorEstimation.m
index d47cb5d..4c26441 100644
--- a/+Simulator/@TwoDimensionalMOT/bootstrapErrorEstimation.m
+++ b/+Simulator/@TwoDimensionalMOT/bootstrapErrorEstimation.m
@@ -9,20 +9,20 @@ function [LoadingRate, StandardError, ConfidenceInterval] = bootstrapErrorEstima
         if ~isnan(CorrelationFactor)
             SampleLength                   = floor(CorrelationFactor);
             NumberOfBootsrapSamples            = 1000;
-            MeanLoadingRatioInEachSample       = zeros(1,NumberOfBootsrapSamples);
+            MeanCaptureRatioInEachSample       = zeros(1,NumberOfBootsrapSamples);
             for SampleNumber = 1:NumberOfBootsrapSamples
                 BoostrapSample                             = datasample(NumberOfLoadedAtoms, SampleLength); % Sample with replacement
-                MeanLoadingRatioInEachSample(SampleNumber) = mean(BoostrapSample) / n; % Empirical bootstrap distribution of sample means
+                MeanCaptureRatioInEachSample(SampleNumber) = mean(BoostrapSample) / n; % Empirical bootstrap distribution of sample means
             end
 
-            LoadingRate = mean(MeanLoadingRatioInEachSample) * ovenObj.ReducedFlux;
+            LoadingRate = mean(MeanCaptureRatioInEachSample) * ovenObj.ReducedFlux;
 
             Variance = 0; % Bootstrap Estimate of Variance
             for SampleNumber = 1:NumberOfBootsrapSamples
-                Variance = Variance + (MeanLoadingRatioInEachSample(SampleNumber) - mean(MeanLoadingRatioInEachSample))^2;
+                Variance = Variance + (MeanCaptureRatioInEachSample(SampleNumber) - mean(MeanCaptureRatioInEachSample))^2;
             end
 
-            StandardError     = sqrt((1 / (NumberOfBootsrapSamples-1)) * Variance) * ovenObj.ReducedFlux;
+            StandardError      = sqrt((1 / (NumberOfBootsrapSamples-1)) * Variance) * ovenObj.ReducedFlux;
 
             ts                 = tinv([0.025  0.975],NumberOfBootsrapSamples-1);  % T-Score
             ConfidenceInterval = LoadingRate + ts*StandardError;                  % 95% Confidence Intervals