aridity hypothesis

(noun)

The theory that the savannah was expanding due to increasingly arid conditions, which then drove hominin adaptation.

Related Terms

Examples of aridity hypothesis in the following topics:

  • The Evolution of Humans

    • The savannah hypothesis states that hominins were forced out of the trees they lived in and onto the expanding savannah; as they did so, they began walking upright on two feet.
    • This idea was expanded in the aridity hypothesis, which posited that the savannah was expanding due to increasingly arid conditions resulting in hominin adaptation.
    • The turnover pulse hypothesis states that extinctions due to environmental conditions hurt specialist species more than generalist ones.
    • The Red Queen hypothesis states that species must constantly evolve in order to compete with co-evolving animals around them.
    • The social brain hypothesis states that improving cognitive capabilities would allow hominins to influence local groups and control resources.

  • Student Learning Outcomes

    • Conduct and interpret hypothesis tests for two population means, population standard deviations known.
    • Conduct and interpret hypothesis tests for two population means, population standard deviations unknown.

  • The Null and the Alternative

    • The alternative hypothesis and the null hypothesis are the two rival hypotheses that are compared by a statistical hypothesis test.
    • In statistical hypothesis testing, the alternative hypothesis and the null hypothesis are the two rival hypotheses which are compared by a statistical hypothesis test.
    • In the hypothesis testing approach of Jerzy Neyman and Egon Pearson, a null hypothesis is contrasted with an alternative hypothesis, and these are decided between on the basis of data, with certain error rates.
    • The concept of an alternative hypothesis forms a major component in modern statistical hypothesis testing; however, it was not part of Ronald Fisher's formulation of statistical hypothesis testing.
    • Modern statistical hypothesis testing accommodates this type of test, since the alternative hypothesis can be just the negation of the null hypothesis.

  • Does the Difference Prove the Point?

    • Rejecting the null hypothesis does not necessarily prove the alternative hypothesis.
    • The critical region of a hypothesis test is the set of all outcomes which cause the null hypothesis to be rejected in favor of the alternative hypothesis.
    • Alternatively, if the testing procedure forces us to reject the null hypothesis ($H_0$), we can accept the alternative hypothesis ($H_1$) and we conclude that the research hypothesis is supported by the data.
    • Rejection of the null hypothesis is a conclusion.
    • We might accept the alternative hypothesis (and the research hypothesis).

  • Student Learning Outcomes

    • Conduct and interpret hypothesis tests for a single population mean, population standard deviation known.
    • Conduct and interpret hypothesis tests for a single population mean, population standard deviation unknown.

  • Student Learning Outcomes

  • Misconceptions

    • State why the probability value is not the probability the null hypothesis is false
    • Explain why a non-significant outcome does not mean the null hypothesis is probably true
    • Misconception: The probability value is the probability that the null hypothesis is false.
    • It is the probability of the data given the null hypothesis.
    • It is not the probability that the null hypothesis is false.

  • Steps in Hypothesis Testing

    • Be able to state the null hypothesis for both one-tailed and two-tailed tests
    • The first step is to specify the null hypothesis.
    • A typical null hypothesis is μ1 - μ2 = 0 which is equivalent to μ1 = μ2.
    • If the probability value is lower then you reject the null hypothesis.
    • Failure to reject the null hypothesis does not constitute support for the null hypothesis.

  • Formulating the Hypothesis

    • A hypothesis is a potential answer to your research question; the research process helps you determine if your hypothesis is true.
    • This is an example of a causal hypothesis.
    • To test this hypothesis, he compared twenty different regional Italian governments.
    • To test this hypothesis, he compared twenty different regional Italian governments.
    • While there is no single way to develop a hypothesis, a useful hypothesis will use deductive reasoning to make predictions that can be experimentally assessed.

  • Type I and II Errors

    • Explain why the null hypothesis should not be accepted when the effect is not significant
    • Instead, α is the probability of a Type I error given that the null hypothesis is true.
    • If the null hypothesis is false, then it is impossible to make a Type I error.
    • Lack of significance does not support the conclusion that the null hypothesis is true.
    • A Type II error can only occur if the null hypothesis is false.