Statistics is a field of practice that analyzes and interprets collected numerical data through the use of various mathematical methods in order to obtain conclusions from the data. Statistics is used to obtain information from a representative sample of a larger sample. The results and conclusions arrived at can be used to make more informed decisions. Statistics can thus be used by different entities such as organizations, businesses, and even the military to make critical decisions. Statistics makes use of critical elements to analyze and make conclusions about the data. This paper discusses various critical elements of statistics such as descriptive statistics, inferential statistics, hypothesis and development testing, selection of appropriate statistical tests, and evaluation of statistical results.
Descriptive Statistics
Descriptive statistics involves the analysis of data which is used to summarize and describe data in ways which configurations could be made from it. Descriptive statistics is thus used to summarize the results of the data instead of simply predicting an outcome. It is used to provide a description of the data which has already taken place and to provide its summary. It can be stated that it is used to describe what is going on within data collections. Descriptive statistics is also important because it is used to provide a visualization of the data where there are large amounts of data (Pyrczak, 2016). It is used to summarize the data and find structural patterns from it.
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Inferential Statistics
Inferential statistics are used to make inferences about data which has been collected. It makes use of a given population which is used to represent the larger population where information is extracted from it. Inferential statistics is usually used in cases where it is difficult to make observations of the larger population. A sample size is thus used as a representative of the larger population. Inferential statistics makes use of generalizations and assumptions in order to make conclusions. The sample size should closely resemble a representation of the entire population.
Hypothesis and Development Testing
The other critical aspect of statistics is hypothesis and development testing. A statistical hypothesis is an assumption about a population which may not or may be true. Hypothesis testing is the procedure used in statistics to reject or accept statistical hypotheses. Park (2015) notes that hypotheses development testing will involve the establishment of a null hypothesis and an alternative hypothesis. These testing methods are used in inferential data because of the difficult to test the full population. Such inferences can lead to errors and this is usually accounted for in the statistical analysis. Hypothesis testing is usually done so as to predict or to make a guess about the outcome of a test and to determine the probability of an event recurring.
Selection of Appropriate Statistical Tests
The selection of appropriate statistical tests is very important for the analysis of the research data. When deciding the type of statistical test that one should perform, one should put into consideration the type of data being analyzed. When considering the type of data, one should consider the measurement scale of the variables. For instances, in cases when there are dependent variables on a continuous scale (such as ordinal, ratio, or interval) and there is an independent variable on the nominal scale, then the appropriate type of statistical test to be used is the t-test. In instances when there are more than two categories of the independent variable, then the type of statistical analysis that is used is variance. Correlation analysis is usually applied in cases when there are two are variables that exist on a continuous scale and can be compared to each other. Chi-square is used when two variables are on a nominal scale and one would like to know their association and its significance (Mertler & Reinhart, 2016). Regression analysis is usually used when the dependent and independent variables exist on different scales but are preferable on continuous scales. The lesson learned from the course is thus how to choose the statistical test based on the nature of variables.
Evaluation of Statistical Results
The evaluation of data is the last step of the statistical analysis. The evaluation of results is important to determine whether the data collected is valid or not. The type of statistical test that has been used and the level of significance of the key parameters are important to determine the validity of the results. Evaluation of statistical results can be done through different types of tests such as a regression or ANOVA test. An ANOVA test is usually done to make sure that an average exists within every test group (Packard, 2018). In case the averages do not exist, the sample size in the analysis could be incorrect. Regression tests can be used to analyze how the variables are connected to each other.
In conclusion, the statistics class has enabled acquire critical skills regarding various topics such as descriptive statistics, inferential statistics, hypothesis and development testing, selection of appropriate statistical tests, and evaluation of statistical results. Understanding the different terminologies in statistics has enabled understanding of how to represent and data graphically. The knowledge of descriptive and inferential statistics can be used to make summaries about a given data and make inferences about the data. Hypothesis testing provides an analysis of assumptions that are been used to analyze the data to determine whether they are true or not. Selection of appropriate statistical tests can be done through the use of variables while evaluation of statistical results can be done through ANOVA and regression tests.
References
Mertler, C. A., & Reinhart, R. V. (2016). Advanced and multivariate statistical methods: Practical application and interpretation . Routledge.
Packard, G. C. (2018). A new research paradigm for bivariate allometry: combining ANOVA and non-linear regression. Journal of Experimental Biology , 221 (7), jeb177519.
Park, H. M. (2015). Hypothesis testing and statistical power of a test. The Trustees of Indiana University
Pyrczak, F. (2016). Making sense of statistics: A conceptual overview . Routledge.
