The above quotation is an example of logos, or an appeal based on reason because<u> Truth is explaining that women deserve equal rights because they endure the same hardships as men.</u>
In her speech, Sojourner Truth, highlights on the idea that women are equal to the men and so equality of work and pay should be given to them. Her speech was very approachable and convincing to the listeners. She gives her examples to prove that women are nowhere behind men. She has alone faced all the troubles and hardships which her life have given to her. She speaks about the issues of the civil rights which included slavery and women suffrage.
What are the underlined words
“Fair is foul and foul is fair” ...
“Brave Macbeth – Well he deserves that name – Confronted him with brandished steel” ...
“Stars hide your fires; let not light see my dark and deep desires” ...
“Come you spirits, that tend on mortal thoughts.
Same Love by Macklemore
Roar by Katy Perry
Thriller by Michael Jackson
"Critical region" redirects here. For the computer science notion of a "critical section", sometimes called a "critical region", see critical section.
A statistical hypothesis is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables.[1] A statistical hypothesis test is a method of statistical inference. Commonly, two statistical data sets are compared, or a data set obtained by sampling is compared against a synthetic data set from an idealized model. A hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis that proposes no relationship between two data sets. The comparison is deemed statistically significant if the relationship between the data sets would be an unlikely realization of the null hypothesis according to a threshold probability—the significance level. Hypothesis tests are used in determining what outcomes of a study would lead to a rejection of the null hypothesis for a pre-specified level of significance. The process of distinguishing between the null hypothesis and the alternative hypothesis is aided by identifying two conceptual types of errors (type 1 & type 2), and by specifying parametric limits on e.g. how much type 1 error will be permitted.
An alternative framework for statistical hypothesis testing is to specify a set of statistical models, one for each candidate hypothesis, and then use model selection techniques to choose the most appropriate model.[2] The most common selection techniques are based on either Akaike information criterion or Bayes factor.
Statistical hypothesis testing is sometimes called confirmatory data analysis. It can be contrasted with exploratory data analysis, which may not have pre-specified hypotheses.
