Statistical Analysis
Statistics is the branch of science that gathers, classifies, analyzes, and interprets data in numbers and makes conclusions about a population based on relevant samples. Statistical Analysis is based on finding patterns in data and making interpretations about the patterns to describe the variability in the study results. Statistical Analysis is used to gather research interpretations in business organizations with large data volumes and studies.
The findings gathered from the analysis are used to draw conclusions that are used to generate solutions or feed into an increase in knowledge in specific disciplines.
Usage and Importance of Statistical Analysis
The primary usefulness of statistical Analysis is that it enables us to do better research and make better decisions by allowing us to draw conclusions by testing our perceptions. It also helps us gain a better understanding of the world around us. Statistical Analysis is also used in:
a) Quality control in product development in supply chain management
b) Optimization and improvement of processes in industries
c) Surveys and customer-based research in business
d) Clinical trials in medicine
Statistical Analysis is essential for the following reasons:
a) It helps us understand data. Statistical Analysis organizes the raw numbers into digestible and understandable bits that make sense.
b) Creates Relationships. It creates relationships between different variables in research as you try to establish an experiment's meaning. You can also analyze the variation between various data points.
c) Decision Making. By identifying trend trends in the data, one can make informed decisions about a specific business.
The Process of Statistical Analysis
1. Data Collection
The first step in Statistical Analysis is the collection of data from various sources. These sources include surveys, questionnaires, experiments, observations, databanks, and databases. The data collected has to be representative of the population under study. It is critical to ensure that the data is accurate and correct.
2. Data Organization
The data collected needs to be organized in a structured and systematic manner. The data organization will involve removing errors and inconsistencies. Data organization ensures that the information is suitable for analysis and that its accuracy is optimum. The data can be organized using spreadsheets like Excel or statistical software like IBM SPSS Statistics and Stata.
3. Descriptive Statistics.
Descriptive statistics involves describing the characteristics of the data. These characteristics include measures of dispersion (standard deviation, range, variance), measures of central tendency (mean, mode, median), and measures of shape (skewness). Descriptive Statistics provides insights into the data distribution and gives us an understanding of it. Descriptive Analysis also describes the quality of the data.
4. Inferential Statistics.
Inferential statistics allows one to draw conclusions from a sample and make accurate predictions about the population. Inferential statistics has two main types: hypothesis testing, which validates and tests assumptions to draw conclusions from a sample about a population, and regression analysis, which estimates the relationship between one or more independent variables and a dependent variable.
5. Data Interpretation and Presentation.
With the data now analyzed, it is now time to make sense of what it is saying. The findings have to be interpreted and presented in a clear, meaningful manner. The data has to be summarized, conclusions drawn, and recommendations made based on the data analysis. Data presentation is done in several ways, including graphs, charts, tables, and digital visualization. By so doing, the data becomes easier to understand and to be interpreted.
