Answer: Monarchy , Aristocracy, oligarchy, and Tyrants I think.
Explanation:
Answer:
B) The chance of committing a Type I error changes from 0.01 to 0.05., E) The test becomes less stringent to reject the null hypothesis (i.e., it becomes easier to reject the null hypothesis), therefore. C) It becomes harder to prove that the null hypothesis is true and G) The chance that the null hypothesis is true changes from 0.01 to 0.05 are all correct answers
Explanation:
The alfa error or type I, refers to the probability error of rejecting the null hypothesis, when it is true, it is the chance of mistake when affirming that an association exists between two variables tested as a cause of an effect on something. i.e: H1: fast food is responsible for diabetes (this is working hypothesis), H0: red hair is responsible for diabetes (this is the null hypothesis). The beta error or type II is related to the size of the sample, it is the chance of accepting something (the null hypothesis) when it is false, depends mostly on having enough measures (or persons under study) so your hypothesis can be proven and be a real representation of the population under study. The statistical significance, namely the p value, can be narrow (p=0.01) or wide (p=0.05), it can be easily understand if we explain it in terms of percentage: you can have 99% (p=0.01) of certainty to affirm that the null hypothesis (the one that you do not believe is true, in the example, red hair as cause of diabetes) is actually wrong or 95% (p=0.05) of certainty to affirm that the null hypothesis (again, the one that you do not believe is true) is actually wrong.
Metrics is the most common name given to computed or derived facts.
A fact is defined as information about the world that is valid, and it is based on evidence rather than personal opinions.
Facts are often classified into different types depending on their characteristics, this includes:
- Analytical facts.
- Derived facts.
- Evaluative facts.
- Stored facts.
- Among others.
In the case of derived facts, these are facts that derive from other facts. For example, if it is known 80% of people in a country are vaccinated, it can be concluded 20% are not vaccinated (derived fact).
Moreover, a derived fact is also known as a metric because derived facts usually measure specific variables based on previous information. This is necessary to differentiate derived facts from other types of facts such as stored facts or analytical facts.
Learn more in: brainly.com/question/19255913
