Curley mistakenly thinks that Lennie is laughing at him with the other men, when, in fact, Lennie is not aware of the drama between Curley and Slim and is simply chuckling to himself with glee thinking about the dream farm. Curley, who has just been embarrassed in front of the workers, picks a fight with Lennie, thinking he'll earn some respect back by beating the much-larger Lennie. Lennie defends himself and attacks Curley only when George tells him to do so. In the scene, George says, "Get 'im, Lennie!" This moment feels very much like a master siccing his dog on someone. Lennie follows George's command and breaks nearly every bone in Curley's hand.
Any kind of fictional scenario you create doesn't require you to consider your characters.
<h3>How may a setting be created for a story?</h3>
A story's setting specifies where and when its plot takes place through the use of literary devices. A story setting, also referred to as a background, can be created from nothing or be based on actual places and times in history (such as a specific city, or the house of a character).
This is produced by the interactions between the characters and their surroundings. Even though it makes sense, adapting this to the surroundings is predictable. When designing your setting, you must carefully evaluate these and decide where to put them.
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fast also it uses many academic words my best guess and if you look at it another way it could be fast and slow but I think its fast
"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.
A person who shows no emotions or is unable to feel them. Someone who shows no empathy for others or doesn't feels sorry for anyone.
