{"id":42574,"date":"2014-03-16T08:00:00","date_gmt":"2014-03-16T02:30:00","guid":{"rendered":"http:\/\/vskills.in\/certification\/tutorial\/?p=42574"},"modified":"2024-04-12T14:19:44","modified_gmt":"2024-04-12T08:49:44","slug":"basic-concepts-3","status":"publish","type":"page","link":"https:\/\/www.vskills.in\/certification\/tutorial\/basic-concepts-3\/","title":{"rendered":"Basic Concepts of Hypothesis Testing"},"content":{"rendered":"\n
\"Basic<\/a>
Basic Concepts of Hypothesis Testing<\/figcaption><\/figure><\/div>\n\n\n

\u00a0<\/p>\n

Basic Concepts of Hypothesis Testing<\/strong>– Hypothesis testing is an act in statistics whereby an analyst\u00a0tests\u00a0an assumption regarding a population parameter. The methodology employed by the analyst depends on the nature of the data used and the reason for the analysis. Hypothesis Testing aids in making fact-based decisions about whether there are different population parameters or whether the differences are due to expected sample variation. In hypothesis testing, an\u00a0analyst\u00a0tests a statistical sample, with the goal of providing evidence on the plausibility of the\u00a0null hypothesis.<\/p>\n

The goal of appropriate Hypothesis Testing is to integrate the Voice of the Process with the Voice of the Business to make decisions based on data to resolve problems.\u00a0<\/p>\n

Hypothesis Testing can help avoid the high costs of experimental efforts by using existing data. This can be likened to:
Local store costs versus mini bar expenses. There may be a need to eventually use experimentation, but careful data analysis can indicate a direction for experimentation if necessary. Statistical analysts test a hypothesis by measuring and examining a random sample of the population being analyzed. All analysts use a random population sample to test two different hypotheses: the\u00a0null hypothesis and the alternative hypothesis. The null hypothesis is usually a hypothesis of equality between population parameters; e.g., a null hypothesis may state that the population means return is equal to zero. The alternative hypothesis is effectively the opposite of a null hypothesis (e.g., the population means return is not equal to zero).\u00a0<\/p>\n

The probability of occurrence is based on pre-determined statistical confidence.<\/p>\n

Decisions are based on:<\/strong><\/p>\n