Hey there!
Have you ever heard of the movie Hidden Figures?
It was about a group of women in times where they were denied a lot of things because of their gender. However, they took a stand, got into NASA, and were a huge part in the math needed to send astronaut John Glenn to space.
Hope this helps!
Answer:
Ms. Dietrich will take the company car because it's bigger than hers.
Explanation:
The sentence that is punctuated correctly is the third option.
This is because, "it's" is properly used as it means "it is" and also the use of the pronoun "hers" is properly used.
"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.
