# 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
A more informative plot is shown in Figure 1B, where boxplots and actual data points are used to represent the observations collected during the experiment with a Student's \(t\)-test for statistical analysis. In each boxplot, the horizontal line crossing the box represents the median, the bottom and top of the 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 quartile range. Furthermore, in Figure 1D, the difference in microbial counts of both methods was calculated and is displayed using a boxplot and the actual data points with a Student's \(t\)-test. In these three figures (Figure 1A,B,D), the plots and statistical analysis are different approaches to the comparison between two methods. Figure 1C presents a three-method comparison; thus a boxplot and the actual data points were used for graphical representation, and an ANOVA was performed to check differences between the means of the three methods. For all these cases, all methods were not statistically significant below an \(\alpha\) value of 0.05, meaning that all methods are comparable to each other. Linear Regression: When conducting a simple linear regression for method comparison, it is essential to establish the standard method (Method 1) and the methods to be compared (Method 2 and 3). Then, the analysis will estimate two parameters that evaluate the relationship between two variables and can be used to estimate the average rate of change (Figure 2). The slope represents the estimated increase in the dependent variable (Method 2 and 3) per unit increase in the independent variable (Method 1); the greater the value of the slope, the greater the rate of change between the variables. The intercept represents the value associated with the dependent variable (Method 2 and 3) when the independent variable (Method 1) is equal to zero [45; 49; 51; 53; 54; 56]. Graphical representation of the linear correlation between Method 2 and Method 3 when compared with Method 1 (\(n=64\) observations per method). The solid line represents the least square regression, and the dots represent the actual data points. For example, Figure 2 shows the graphical representation of linear regression analysis between Method 2 and 3 when compared with Method 1. Ideally, the slope should take a value of 1, suggesting that for any 1 Log increase using any of the alternative methodologies, there will be an increase in 1 Log, using the standard methodology. In addition, confidence intervals for the slope are estimated for statistical analysis purposes to verify with a certain level of confidence that the estimated slope is different from 1 (Table 1). Moreover, the intercept measures the similarity of the total magnitude between the methods. Ideally, the intercept should be equal to 0, and the \(p\)-value for the intercept can be used for statistical significance, as it is calculated evaluating a null hypothesis that the intercept is equal to zero; thus, if significant, discrepancies between methodologies can be determined (Table 1). For this example, the 95% confidence interval of the slope for Method 3 did contain a value of 1, and the \(p\)-value for the intercept was not significant (\(p\) = 0.653); therefore, the methodologies are not different. (3) Proportion Comparison: the last two examples covered the comparison between two enumeration methodologies, but sometimes methods will give results that follow binomial distributions. A clear example in microbiology is the presence or non-detection of a certain microorganism. The basic idea behind this type of experiment is to compare if the proportion of samples found in each category are statistically different [57; 58; 61]. [PERSON]'s chi-square test is often the best approach when two categories are created (Table 2). A significant \(p\)-value suggests that methods are not the same at classifying a certain variable in both categories.