Wow u got alot of work to do and I believe A. don't really believe me I'm not an expert
Answer:
The word patriot signifies a person who loves his or her country and is ready to boldly support and defend it. That meaning has endured since the word's arrival in English in the 16th century, but it has not marched through the years unchallenged.
Ultimately derived from Greek patrios, meaning "of one’s father," patriot entered English via French patriote—meaning "fellow countryman" or "compatriot"—during a time of political unrest in western Europe that was characterized by infighting among fellow countrymen—especially among those of the Protestant and Catholic faiths. For much of the 17th century, words like good were attached to patriot to distinguish patriots who shared both a love of country and a common allegiance from those having opposing beliefs and loyalties: to be deemed a "good patriot" was to be a lover of country who agreed on political and/or religious matters with whoever was doing the deeming.
"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.
