# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 4 Type of Food Safety Experiments
### Methodology Comparison
#### 4.1.1 Data Visualization and Data Analysis  
The objective for this type of experiment is to show how different methodologies compare and contrast for detecting or enumerating a certain microorganism at a specific matrix. Therefore, the outcome should be any measurement that shows how comparable the methods are, such as \(p\)-values, confidence intervals, or correlation coefficients. There are several approaches to prove with a certain level of confidence that the methods are similar or different. However, the selection process requires an understanding of the statistical analysis and interpretation of the results. 1. Means Comparison: when comparing two methods for enumeration of microorganisms, it is intuitive to determine whether the microbial means of both methods are equal [50; 52; 55]. The basic idea is quite simple: if the difference between the sample means is far enough from zero, there will be enough evidence that both methods are different. As a result, a Student's \(t\)-test for paired samples or the non-parametric alternative [PERSON]'s test for paired samples are the best alternatives (Figure 1A,B,D). A significant \(p\)-value will show enough evidence that both methods are different when enumerating a certain microorganism on a specific matrix. When more than two methods are to be compared, an ANOVA test followed by a pairwise comparison \(t\)-test or the non-parametric alternative Kruskal-Wallis test followed by a pairwise Wilcoxon's test can be used for these purposes (Figure 1C). The multiple comparison test will be only performed if the null hypothesis is originally rejected. Figure 1: Different graphical representation and statistical analysis approaches to evaluate method comparison studies based on means comparison. (**A**) Microbial counts (Log CFU/mL) of two different methods (Method 1 and Method 2), (\(n\) = 30 observations per method). The bar chart represents the mean counts for each method, and the error bars represent \(\pm\) 3 SE. Different letters are significantly different according to [PERSON]’s test for paired samples at \(p\)\(<\) 0.05. (**B**) Microbial counts (Log CFU/mL) of two different methods (Method 1 and Method 2), (\(n\) = 30 observations per method). In each boxplot, the horizontal line crossing the box represents the median, the bottom and top box are the lower and upper quartiles, the vertical top line represents 1.5 times the interquartile range, and the vertical bottom line represents 1.5 times the lower interquartile range. Boxes with different letters are significantly different according to \(t\)-test at \(p\)\(<\) 0.05. The points represent the actual data points. (**C**) Microbial counts (Log CFU/mL) of three different methods (Method 1, Method 2, and Method 3), (\(n\) = 30 observations per method). In each boxplot, the horizontal line crossing the box represents the median, the bottom and top box are the lower and upper quartiles, the vertical top line represents 1.5 times the interquartile range, and the vertical bottom line represents 1.5 times the lower interquartile range. Boxes with different letters or are significantly different according to ANOVA followed by pairwise comparison test adjusted Tukey at \(p\)\(<\) 0.05. The points represent the actual data points. (**D**) Difference in microbial counts (Log) between two methods (Method 1 and Method 2), (\(n\) = 30 observations per method). In each boxplot, the horizontal line crossing the box represents the median, the bottom and top box are the lower and upper quartiles, the vertical top line represents 1.5 times the interquartile range, and the vertical bottom line represents 1.5 times the lower interquartile range. Boxes with different letters or are significantly different according to \(t\)-test at \(p\)\(<\) 0.05. The points represent the actual data points. Figure 1A shows a bar chart plot with error bars (\(\pm\)3 SE) as a representation of the mean and variability of the two methods. A non-parametric statistical test was used for descriptive purposes.