5 Factors You Should Keep In Mind Before Presenting Statistical Results
The present scenario is a cut-throat digital era where the need for understanding the right way to present the statistical outcomes has become foremost important to move ahead among others. The organisations are spending millions of bucks on collecting and analysing data outcomes but it can be seen that most of the time it is harder for the audience to understand and utilise the data in a profitable way. In this article, we are listing the top five important points to keep in mind before you present your statistical results before the audience. 1| Importance Of Analytics Knowledge Different companies use different methods of statistical knowledge and analysis, so it is a crucial part to have the knowledge of which statistical knowledge the audiences understand. While reporting statistical results, if you know the statistical understanding that the audiences have, it will be easier for you to describe the methods as well as for them to get the grip of your topic. There are various statistical tools to get the hold of data. Software designed for statistical analysis can help in interpreting results in a smooth and easy way. Tools like Statistical Package for the Social Sciences(SPSS) widely used for the statistics software package within human behavior research. Matlab, an analytical platform and programming language used widely by engineers and scientists. Statistical Analysis Software (SAS), a statistical analysis platform offers options to use GUI or to create scripts. Minitab offers a range of both basic and advanced statistical tools for data analysis.  All you need to know is using the right tool for the right analysis of data. 2| Understand The End User The first and foremost thing after setting the statistical environment is to understand and question yourself before you present your statistical results to the end users is that who the audience is, whom you are dealing with, whether they are new to the field or the advanced ones. The result is to be mended precisely according to these categories of the audiences. The information that is to be provided to the audiences needs to be altered with their point of view as well as their needs. It is crucial to make the audience understand the statistical significance which is mainly built on simple ideas such as hypothesis testing which is a technique for evaluating a theory using data, the normal distribution where the application comes from assessing the data points in terms of standard deviation and p values which is the probability of observing results at least as extreme as those measured when the null hypothesis is true. 3| Make Your Result Understandable You need to present your results and data in such a way that it makes sense to the audience. It is not always necessary for users to know the information as a whole, so keeping in mind about the relevant topic of what to make them understand will help you grasp the attention of the audiences sooner than you think. When analysing data, it is important to always start from the review of your research goals. In our earlier article, we had listed down the six most common statistical errors made by data scientists. It is very important to cross-check the statistical result before presenting it to the audience because the underlying principle in statistics and data science is the correlation is not causation which means just because two things appear to be related to each other doesn’t mean that one is the reason behind the cause of the other. For instance, the statistical graph between US spending on science, space, and technology correlates with suicides by hanging, strangulation, and suffocation. 4| Convincing The Audience After presenting your statistical results, the first thing that strikes us is how the audience will react to your analysis and what is the probability of using your result by the end users. Statistical results that help in improving the process of an enterprise will be more beneficial than starting a process from the ground level. An important note while working on a statistical project is how much the executives are going to save after spending bucks on the project or what will be the profit at the end. 5| Be Prepared For The Reactions While presenting your report, make sure you prepare for the questions and arguments by the audiences. Humble answers for questions like why your results should be taken into the count or whether the results are validated or not should be kept in mind during presenting your statistical results. Bottom Line If you want to transform your statistical results into profitable decisions for an organisation, start preparing the right set of points to achieve the relevant insights. You have to prepare your results in such a way that the end-users can independently find it readable as well as understandable. It is always great to be a little ahead among others in a room. It is also very crucial that the external parties who will use your results should understand the statistical result designed by you in an easy way. Knowing that different end users have different needs, preparing the statistical results according to that insight will surely prove to be a fruitful one in your hard work.
The present scenario is a cut-throat digital era where the need for understanding the right way to present the statistical outcomes has become foremost important to move ahead among others. The organisations are spending millions of bucks on collecting and analysing data outcomes but it can be seen that most of the time it is […]
The present scenario is a cut-throat digital era where the need for understanding the right way to present the statistical outcomes has become foremost
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["Go", "data science", "programming_languages:R", "AI", "programming_languages:Go", "Git", "analytics", "GAN", "R"]
["AI", "data science", "analytics", "R", "Go", "Git", "GAN", "programming_languages:R", "programming_languages:Go"]
A Technical Journalist who loves writing about Machine Learning and Artificial Intelligence. A lover of music, writing and learning something out of the box.