Hypothesis Testing and Life Expectancy

Abstract

Data science revolves around the idea of making moves/ choices based on past information available. However, data is a broader term. There is raw data, which is collected by using surveys and other methods, it may contain the information we need, but it also contains various other non-required information, which is removed from the raw data by processing it. The data now recovered is vital information. Data Science is used in predicting trends and forecasting events. Hypothesis testing is a process through which we verify and predict data. In the testing phase, we consider two ideas or theories that are yet not proven. Through statistical analysis and mathematical relations, we tend to make decisions. Hypothesis testing is a one-result process, which means that there is only one result. The theory can be right or wrong. Hypothesis testing just simplifies the decisions to a point of yes or no. It combines both at a fundamental level and is quite easy to understand and begin with. In addition to that, the testing involves methods currently being taught in our syllabus, so a side-by-side application helps open our minds toward the subject.