The Effects of Parametric, Non-Parametric Tests and Processes in Inferential Statistics for Business Decision Making
—A Case of 7 Selected Small Business Enterprises in Uganda

The article gives a critique of parametric and nonparametric tests and processes of inferential statistics in forecasting customer flows in 7 selected small business enterprises in Uganda. Forecasting is one of the decision making tools in a business enterprise. This may include forecasting customer flows, volumes of sales and many others. This is a vital component of small businesses success. In the long run, what drives business success is the quality of decisions and their implementation. Decisions based on a foundation of knowledge and sound reasoning can lead the company into long-term prosperity; conversely, decisions made on the basis of flawed logic, emotionalism, or incomplete information can quickly put a business out of commission. In many instances, business decisions have been guided by parametric tests and processes and /or non-parametric tests and processes of inferential statistics, which have subsequently affected the futures of business differently. As we refer to population mean knowledge for hypothesis testing using parametric tests, we only refer to mediums for samples, for nonparametric tests. A parameter is a characteristic that describes a population. These may include μ (the Mean), δ² (the variance) of a distribution. We commonly refer to the normal distribution, when it is symmetric, with the measures of central tendency (Mean = medium = mode). Usually these parameters are very useful,