Statistical hypothesis testing
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Statistical Hypothesis Testing
Statistical hypothesis testing (pronunciation: /stəˈtɪstɪkəl haɪˈpɒθɪsɪs ˈtɛstɪŋ/) is a method used in statistics to evaluate two mutually exclusive statements about a population to determine which statement is best supported by the sample data.
Etymology
The term "hypothesis testing" is derived from the Greek word "hypothesis" (meaning "to put under" or "to suppose") and the English word "test", which in this context means "a procedure intended to establish the quality, performance, or reliability of something".
Related Terms
- Null Hypothesis: The hypothesis that there is no significant difference between specified populations, any observed difference being due to sampling or experimental error.
- Alternative Hypothesis: The hypothesis that is contrary to the null hypothesis. It is usually taken to be that the observations are the result of a real effect.
- Significance Level: The probability of rejecting the null hypothesis when it is true.
- P-value: The probability level which forms basis for deciding if results are statistically significant or not.
- Type I and Type II Errors: Type I error occurs when the null hypothesis is true, but is rejected. Type II error occurs when the null hypothesis is false, but is erroneously accepted.
Process
Statistical hypothesis testing involves four steps:
- State the hypotheses. This involves stating the null and alternative hypotheses. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false.
- Formulate an analysis plan. The analysis plan describes how to use the data to accept or reject the null hypothesis.
- Analyze sample data. Using the analysis plan, the data is analyzed.
- Interpret the results. If the sample findings are unlikely, given the null hypothesis, the researcher rejects the null hypothesis.
See Also
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