# Modernization Data Analysis and Visualization for Food Safety Research Outcomes
## 1 Introduction  
Constant research in food safety is crucial for reducing foodborne illnesses because they impact society, health, and the economy of the food industry. Some of the benefits of performing research in this field are improving economic productivity and public safety, reducing healthcare costs for individuals affected by foodborne pathogens, increasing the shelf life of food products, and providing a safer food supply to consumers [1]. In the United States, it is estimated that foodborne diseases caused by pathogens affect 9 million people (about half the population of New York) and cause about 56,000 hospitalizations and 1300 deaths each year [2]. The four main pathogens of importance in terms of foodborne illnesses are _Salmonella_, _Escherichia coli_ O157:H7, _Listeria monocytogenes_, and _Campylobacter_[3]. About 80% of foodborne illnesses are linked to _Salmonella_ and _Escherichia coli_ O157:H7[3]. Also, around 75% of _Salmonella_ illnesses are mainly derived from chicken, fruits, pork, seeded vegetables, beef, or turkey [3], while _Escherichia coli_ O157:H7 is linked to vegetable row crops and beef [3]. _Listeria monocytogenes_ illnesses are associated with dairy products, fruits, and vegetable row crops [3]. Although _Campylobacter_ is one of the major pathogens that causes foodborne illnesses in United States, estimates linked to this pathogen are ambiguous because they contrast considerably with non-outbreak-associated illnesses [3]. This valuable information demonstrates the importance of microbial foodborne pathogens in our society, showing that data are a powerful dissemination tool [4; 5]. Collection of data in a proper manner that follows procedure and uses accurate methods is crucial to obtaining reliable data when conducting an experiment. However, it is key to perform an assertive statistical analysis and data visualization for a correct assessment of microorganisms [6; 7; 8]. A clear statistical summary and presentation of the data is important for the reader to easily process and comprehend results. The summary of data in an absorbable manner can be achieved through statistics, which is a science composed of two main branches, descriptive and inferential statistics. Descriptive statistics summarize a data set and disseminate it in an accessible manner, while inferential statistics lead to conclusions from a small sample size that is exposed to random variation and make predictions and generalizations about a population [9; 10; 11]. In microbiology, correct estimations of microbial concentrations and their presence in food products are necessary for public health regulations and decisions because they comprise the interpretation and description of models that can subsequently affect public health [12; 13]. Microorganisms are discrete objects that cannot be measured precisely; therefore, microbial concentrations are measured by enumeration or detection of the objects in a fixed sample size prior to performing an experimental trial [14; 15; 16]. These approaches are broadly used in the food and health industry, and these estimates are mostly compared to determined concentrations or targets, which can decrease the accuracy of the data, detection, and enumeration of microorganisms [17; 18]. Microbial data are analyzed and presented in a lognormal distribution to describe the variability of bacterial concentrations, which allows the data to be interpreted following a normal distribution [19; 20; 21; 22]. Some of the challenges of log transforming microbial data are obtaining non-detectable values and back-calculating concentrations from microbial counts [12]. Therefore, a correct data analysis should be performed to avoid biased results. Some of the misconceptions that can be dismissed regarding inaccurate data analysis are due to crucial differences between discrete and continuous data. These are obtained using analytical chemistry methodologies, and they expose bias obtained by statistical analysis within chemistry data and its dissipation into discrete microbial data [12].