Definition/Introduction

Statistics, as a science, is the scientific process of acquisition and management of a given set of data. In the medical field and other life sciences, the term “biostatistics” is often used instead to emphasize its application to medicine and health. Statistics is used to provide information on the given health situation and guide healthcare professionals in the decision-making process, whether as part of the research study or as part of clinical work.

The application of statistics undergoes a series of steps creating a cycle of scientific activities. Usually, it begins with the acquisition of health data. This collection of data involves gathering health-related information through the use of data collection tools (e.g., survey questionnaires) to accurately acquire details pertinent to a given study. Collecting data directly from the respondents is termed as primary sources of data. If the researcher wants to use a given set of data that were collected beyond the scope of the study (e.g., vital statistics and health statistics), then these are termed as secondary sources of data. Before moving on to the next step of the process, the accuracy and reliability of the data collection must be confirmed since any alterations or misinformation during this process would inevitably affect the analysis and interpretation of data on hand.

Data management, on the other hand, employs the organization and analysis of health data. Data can be organized in numerous ways, so every researcher should only use methods depending on the specific goal of the study. For example, if the statistical data must be interpreted as individual units, it can be organized in the form of a raw data or data series (e.g., arranged in arrays or alphabetical order). This is usually done in studies having a small population (e.g., case studies, case series).  Otherwise, if the data needs to be described using frequency distribution, it can be organized either as discrete or continuous data series using frequency tables. This collection process is frequently used in studies with a larger study population. It is important to note that the best method of organizing statistical data primarily depends on the type of variable (e.g., qualitative or quantitative) and its level of measurement (e.g., nominal, ordinal, interval, ratio). Arriving at all possible data organizations may not be necessary if, and only if, this will give the best information to the researchers about the objectives of the study.

The use of appropriate methods to organize data will lead to its accurate analysis. In descriptive data analysis, the use of narratives, tables, graphs, and charts can be sufficient to describe the study variables. In the inferential analysis, the researcher needs to either estimate specific clinical or health parameters or perform hypothesis testing. Several versions of data analysis software are available for use according to the type of research work.[1]

Eventually, accurate and reliable interpretation follows from properly conducted data analysis. This step focuses on generating correct information based on the findings while relating it to the context of the topic under study. The current generation of discoveries, conclusions, and hypotheses will make future researchers capable of studying its underlying issues and restarting the statistical process, creating a continuous cycle of collecting, organizing, analyzing, and interpreting data.

Issues of Concern

There are some issues with statistics as applied in the healthcare setting. Most of these issues are encountered from research studies, both from community and clinical researches. These include, but not limited to, the following points enumerated below:

Data collection

• The integrity of the data collection[2] 
• Data collection about “dying patients”[3]
• Advantages and disadvantages of data collection approaches[4][5] 
• Researcher-participant partnership[6]

Data organization (and presentation)

• Use of relational database[7]
• Creation of frequency distribution: from tabulation to graphical representation[8]
• Type of charts based on the data analysis method[9]

Data analysis

• Statistical analysis of small area health studies[10]
• Misconceptions about data analysis and statistics[11]
• Limitation of data and its measurement in studying health disparities[12]
• Ethical issues on the use of secondary data analysis[13]

Data interpretation

• The interpretation of p-values[14][15] 
• Steps to data summarization[16]
• Differences in the application of clinical and statistical significance[17][18] 

Clinical Significance

Although this is not a definite part of the statistical process according to previous and current references on the topic, the "utilization of data in the healthcare setting" can still be an additional part of an overall process.  The use of relevant statistical findings and conclusions is vital in the decision-making process of both internal and external health stakeholders.

Just as there is a data analysis plan made before any study is implemented, a careful plan is required on how statistical results shall be shared with the appropriate audience. Graphical presentation as used in descriptive studies can effectively aid in the understanding of both technical and non-technical staff. The introduction of statistical output in presenting the results to the stakeholders may not always be beneficial at all times. Instead, the meaning and relevance of the statistical test and the practical application of its conclusion should be given more emphasis. Determining the recipients of the statistical findings will help in defining the method of disseminating specific statistical information.[19]

Generally, the importance of statistics as a tool for the execution of health research and the development of new knowledge and understanding in the healthcare practice has already been proven both in the past and in contemporary situations. While statistics carry its specific language, like medicine, science, and other technical areas, each healthcare professional in clinics, hospitals, laboratories, and health industries must understand the basic concepts of statistics. It must always be emphasized that the application of statistics in health and medicine is meant to help the healthcare team to have a deeper understanding of health-related variables and events, and not to confuse them in any other way, for instance, due to the misuse and abuse of statistics[20], which could, indirectly yet clinically, impact patient outcomes in the future.

Nursing, Allied Health, and Interprofessional Team Interventions

All interprofessional healthcare team members need to possess at least a baseline understanding of statistics pertaining to the evaluation of raw healthcare data and studies. This knowledge is essential to updating care regimens and making medical decisions. As new data comes available, it is incumbent on all healthcare team members to incorporate these new findings into tangible action to benefit patients, leading to better outcomes. [Level 5]