Statistics is a mathematical discipline which is all about the collection of data, analyzing the same, interpreting, and presenting it in the form of information. Research has indicated that statistics is of great essence to management considering that it builds the confidence of those that are in managerial positions, especially when dealing with the process of making decisions as they are able to make selections that are smart. The result of statistical analysis would offer the organization some accurate form of information that they can easily use for reference purposes, besides the case of avoiding problems that would occur in the future. In that case, it is crucial to understand the various elements of statistics as they help in determining the best decisions to go by from the management point of view.
The first one is descriptive statistics that are short coefficients of description that give a summary of a particular set of data that appears in two forms; a part of the whole data, or a representation of a few selections (Ehrenberg & Montagnon, 2017). They can involve the measures of variability like the minimum and maximum variables, the variance, and the standard deviation. They can also include the measures of central tendency like the mode, mean, and median. In other words, descriptive statistics aid in understanding the features that a particular set of data depicts. For the sake of making the decision process simpler for managers, descriptive statistics make a complex set of data easier to comprehend. For instance, descriptive statistics could give a return behavior or the account history of a business through data analyses on investments, as that would be helpful for the managers to make better decisions on investment in future days.
The second one is inferential statistics uses data analysis to get the properties of the whole population from a sample representation and using the same to infer or make assumptions for all the data. It differs from descriptive statistics as the later does not make assumptions based on the larger set of data. The first process involves choosing a model of statistics that would help in the generation of the data set sample, followed by deducing assumptions and other characteristics from the selected model. In other terms, inferential statistics helps in giving answers to questions regarding a population or a data set that have not been tested before (Ehrenberg & Montagnon, 2017). Despite the fact that it relies on the probability the sample data, to make the same assumptions true for the entire set means that there is a need of creating the right conditions that would generate the most appropriate results for the selected model for testing. For instance, inferential statistics can be used to find out the probability of how some goods will be purchased in a given region, hence enabling the management get to know whether to invest in that area or not.
The third element is hypothesis development and testing, where a hypothesis is a statement of what the research aims at investigating. It enables them to have speculation of what to expect when they do the investigation. There are various methods that can be used for the generation of hypotheses, but inductive reasoning in the guide where one observes and develops a theory from what they see. The first step is to have a hypothesis that is tested using a number of means like observation and other forms of experimenting. To the researcher, the intention is to create a hypothesis and prove that it is false so that they can come up with another one until they cannot falsify it any further (Simonsohn, 2013). A hypothesis is developed in the form of a statement describing a set of facts which allow for more investigation to take place. The main advantage of a hypothesis to a manager is that it allows for the creation and testing of the same to prove whether it is something that can be dropped or adopted, hence improving on the decision-making skills.
The fourth statistical element is statistical tests and their selections. Statistical tests are some of the most crucial tools for analyzing quantitative data, with the main intention of being getting to find out whether it is safe to adopt a process based on a hypothesis. While there are several ways of conducting statistical tests, the most difficult part is related to selecting the most appropriate method of testing when dealing with different kinds of data. In that case, the test to use will depend on a few factors, including the type of data in use, the form of distribution the data is following, and the nature of the variables (Simonsohn, 2013). Once these factors are in place, it becomes easier for the researcher to get the test needed. In that case, the most common types are the correlational, Pearson correlation, Spearman correlation, and the Chi-square. The correlational test looks at what association exists between two different variables. The Pearson correlation varies differently as it checks the relationship between variables that are continuous in nature. The Spearman correlation tests for the strength of the relationship between variables that are ordinal, while at the same time there is no dependence on the assumption depicted by data that is normally distributed. The Chi-square test is used for determining the strength of the relationship between two variables that are categorical. While there are many other tests, the intention is to observe and make inferences that would make it easier to judge from the observable pattern of the results (Weise & Chiong, 2015). As a manager, one can make a decision as they would get to find out what is real and that which may not work in the organization.
The final element of statistics is evaluating a statistical result which is a quantitative technique that is used in getting the probabilities between groups of data of the outcomes. The main aim is to clearly elaborate on the patterns and trends that the researcher realizes from the topic that is under investigation. For instance, if a marketing manager would want to know if a product will be accepted in the market willingly, they would do a sample introduction to a given market population and see how the buyers would behave. He would then do an analysis of the results to find out if the product has an accuracy that is probable or accurate (Weise & Chiong, 2015). Analyzing the null hypothesis, in this case, means testing the validity of the data that has been collected. For the effective evaluation, it is crucial that all the gathered data is pooled in groups that hold similar data so that the researcher does the calculation of the statistical parameters. For instance, the P-value is of great essence as it helps in the determination of how valid the null hypothesis is. If it is not, then its opposite is true and needs to be carried out (Ehrenberg & Montagnon, 2017). If it is true that a given product has a negative reception within a given market, it makes it easier for the management to make a decision based on what they learn from the analysis of the null hypothesis.
In conclusion, statistics in an organization will always be present as they are useful in the decision making the process for the management. It is of great essence that there is data gathering at all times as that gives an overview of what is going on. In that case, the implementation of various elements of statistics is unavoidable, as they offer the chance to critically analyze what is collected at regular intervals. The outcome is then useful for the management as they can know what will work or what is working, and that which will not or is not working. The decision will entirely depend on the accuracy of the statistical analysis carried out from time to time.
Ehrenberg, A., & Montagnon, P. (2017). Foundations of Statistics: A Survey for Managers. Journal Of The Royal Statistical Society. Series A (General), 144(3), 377. doi: 10.2307/2981817
Simonsohn, U. (2013). Evaluating Statistical Replication Results. SSRN Electronic Journal, 4(2). doi: 10.2139/ssrn.2259879
Weise, T., & Chiong, R. (2015). An alternative way of presenting statistical test results when evaluating the performance of stochastic approaches. Neurocomputing, 147, 235-238. doi: 10.1016/j.neucom.2014.06.071
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