Basic Concepts of Hypothesis Testing– Hypothesis testing is an act in statistics whereby an analyst tests an 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 analyst tests a statistical sample, with the goal of providing evidence on the plausibility of the null hypothesis.
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.
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 null 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).
The probability of occurrence is based on pre-determined statistical confidence.
Decisions are based on:
- Beliefs (past experience)
- Preferences (current needs)
- Evidence (statistical data)
- Risk (acceptable level of failure)
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