Alternative Statistical Hypothesis
(Redirected from alternative hypothesis)
- AKA: Maintained/Research Hypothesis.
- See: Significance Test, Predictive Power, Explanatory Power.
- (Wikipedia, 2016) ⇒ https://en.wikipedia.org/wiki/alternative_hypothesis Retrieved:2016-9-15.
- In statistical hypothesis testing, the alternative hypothesis (or maintained hypothesis or research hypothesis) and the null hypothesis are the two rival hypotheses which are compared by a statistical hypothesis test.
- (The Pennsylvania State University, 2016) ⇒ Setting the Hypotheses: Examples. (n.d.). Retrieved 2016-08-07 from http://onlinecourses.science.psu.edu/stat100/node/63
- Example 11.2. Hypotheses with One Sample of One Categorical Variable
- About 10% of the human population is left-handed. Suppose a researcher at Penn State speculates that students in the College of Arts and Architecture are more likely to be left-handed than people found in the general population. We only have one sample since we will be comparing a population proportion based on a sample value to a known population value.
- Research Question: Are artists more likely to be left-handed than people found in the general population?
- Response Variable: Classification of student as either right-handed or left handed
- State Null and Alternative Hypotheses
- Null Hypothesis: Students in the College of Arts and Architecture are no more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Art and Architecture = 10% or p = .10).
- Alternative Hypothesis: Students in the College of Arts and Architecture are more likely to be left-handed than people in the general population (population percent of left-handed students in the College of Art and Architecture > 10% or p > .10). This is a one-sided alternative hypothesis.
- (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Alternative_hypothesis
- … The concept of an alternative hypothesis in testing was devised by Jerzy Neyman and Egon Pearson, and it is used in the Neyman–Pearson Lemma. It forms a major component modern Statistical Hypothesis Testing. However it was not part of Ronald Fisher's formulation of statistical hypothesis testing, and he violently opposed its use. In Fisher's approach to testing, the central idea is to assess whether the observed dataset could have resulted from chance if the null hypothesis were assumed to hold, notionally without preconceptions about what other model might hold. Modern statistical hypothesis testing accommodates this type of test since the alternative hypothesis can be just the negation of the null hypothesis.