Statistics is very essential in the place of work in the world today. The statistics are used to promote various operations that can promote the strategic planning and decision making. Statists are very essential in the collection of facts that are very essential in the generation of information. The statistics are used in the place of work in the generation and establishment trends from the historical events. The statistics (Sheskin, 2007). The information that is generated from the statistics can be applied in the improving the level of service delivery because it provides an insight on the weak areas of operation and in devising strategies that can be used in improving them.

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Descriptive statistics describes the quantitative descriptions of the main structures about certain information (Tukey, 2007). Through descriptive statistics, the basic features of data in the activity. There are various type of descriptive statistics in adult health nursing. These statistics are used in generation evidence-based practices in advanced clinical practice. An example of the descriptive statistics includes the mean. The mean can be used in finding the average age of the number of patients with a certain clinical health condition. All the measures of central tendency and measures of spread are very critical in the generation of the trends that are required in clinical practice. The mode and median can also be used in finding categories in characteristics that can be defined along various socio-cultural alignments. The descriptive statistics are very essential in the description of various types of group data while combining the numerous forms of tabulated, graphical and commentary descriptions (Sheskin, 2007).

Health care services are given to various types of people. This is because these services are very essential for people from various backgrounds. The society is the formation of people with different values and beliefs. The structural alignment of the society is one of the components that generated various gaps and disparities among various types of people with respect to the features and characteristics associated with healthcare. This data is descriptive because it can be structured and analyzed in groups so as to assist in making conclusions and inferences based on the societal orientations. Raw data from the healthcare services is very difficult to understand and visualize. Consequently, putting the data in a descriptive form enhances the understanding of the specific features of the data.

Inferential data can be used to generate some information about the population group that is generated from the sample data (Hand, 2006). The samples that are collected from the fields are assumed to be typical representations of the whole population in the field. In collecting health information about various subjects of health concern about in order to take the corrective measures, the information taken from the populations. There are various sources of health bias in the society. The bias can be identified along age, gender, race or economic status. Therefore, inferential data must be taken to be a representation of all social groups in the society to enhance its credibility.

There are some types of data that are not collected in my place of work and need to be collected in order to improve the level of service delivery (Sheskin, 2007). Qualitative data should also be collected in the place of work. This type of data can be essential in promoting the continuous quality improvement initiatives. This data can also be used to measure the success or failure of various health initiatives. The quantitative data can be used to have the researchers have sufficient information that can assist in generating the best practices that are very essential in upholding both patient and expert safety.

In order to improve the quality of health research, it is important to improve the quality of the data collection techniques. This will assist in promoting the quality of the data and the quality of the eventual use of the data. Promoting the collection of data to embrace the standardized and cohesive way is very instrumental towards improving the data collection schemes. The most practical and realistic way to achieve this to ensure that the staff members involved in the data collection activities undergo intensive training so as to break the barriers that assist in enhancing the quality of data. Equipping the staff members with the correct and recommended estimation techniques and the ethical and moral protocols associated with the data can improve the collection of the data.

The four level of data measurement is very important in the utility of data in our organization. The nominal scale is used in naming the attributes uniquely. Gender, age, color and social status are some of the variables that are measured on a nominal scale (Hand, 2006). On the ordinal scale, there are various attributes that can be ranked in order to obtain some meaning from the information. The interval scale is used in establishing the meaning in the gap between the attributes that are used in an organization. The ratio measurement is used in establishing a relative representation between various attributes identified in data. All these data measurement scales are very important in the creation of meaning in the data that is collected and this is important in the making inferences (Hand, 2006).

Accurate interpretation of statistical data is very important in the research process. The accuracy of the interpretation can also influence the quality of the decisions made (Sheskin, 2007). Accurate interpretation of the information is very crucial towards putting the credibility to the research process. Credible data can be used in generating reliable information. Therefore, accurate interpretation of statistical data is a critical construct of decision making and planning.

    References
  • Hand, J. (2006). Measurement theory and practice. London: Arnold.
  • Sheskin, J.D. (2007). Parametric and Nonparametric Statistical Procedures. Boca Raton: Chapman & Hall.
  • Tukey, J. (2007). Data Regression and analysis. Boston: Addison-Wesley