Answer:
I have never seen an Elephant
My cousin lives in the USA
We will go for a walk we are finished with our lesson
Listen, somebody is playing the piano so beautifully.
My parents will buy me a new bike next month.
We had a wonderful time in the mountains last summer.
If we go to the village, i will go fishing.
Diana washed the fruits before she made the salad.
Explanation:
Answer:
all of the above
Explanation:
he was a great man who did great things
"Justice as Fairness: Political not Metaphysical" is an essay by John Rawls, published in 1985. ... In it he describes his conception of justice. It comprises two main principles of liberty and equality; the second is subdivided into Fair Equality of Opportunity and the Difference Principle.
Either the inevitability of fate, or the repetition of the world (his dad went crazy and was killed by his kids who he tried to kill, then cronus went crazy and was killed by his kids who he tried to kill.
"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.
