For the first one, I would start by mentioning the characters crime, add something about how it was inexcusable, then incorporate a condensed precis on the characters situation (leaving out any bits that could embody the characters freedom). And end with a statement that discloses that your appeal is unarguable because (enter main argument here) and that is why so and so should be imprisoned.
<span>For the second question, I would personally choose the feminist because it would in theory be easier to explain their basic philosophy and how it is affecting the plot. Because I don't know who the character is, I can't really elaborate further. But please contact me if you need any more help, I'll do what I can. </span>
I think that the answer is A: to avoid punishment
Answer:
I didn't watch TV yesterday.
When did you move to San Francisco?
"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.