Short Answer 1. What is the purpose of doing statistical inference? (3 pts) 2. W

Question

Short Answer 1. What is the purpose of doing statistical inference? (3 pts) 2. When performing a hypothesis test, what is the goal? (3 pts) Why do we need to check "Assumptions and Conditions" when doing statistical inference?(3 pts) 3. 4. The Central Limit Theorem is the basis for using the Normal distribution as the sampling distribution of what statistic? (2 pts) What feature of the Central Limit Theorem makes it so valuable in statistics? (3 pts) 4low 5. What are the two ways to decrease the margin of error of a confidence interval? (4 pts

Explanation / Answer

SOLUTION :-

1)

Statistical inference is the process of deducing properties of an underlying probability distribution by analysis of data. Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates.

2)

A process by which an analyst tests a statistical hypothesis. The methodology employed by the analyst depends on the nature of the data used, and the goals of the analysis. The goal is to either accept or reject the null hypothesis.

3)

We can develop this understanding of sound statistical reasoning and practices long before we must confront the rest of the issues surrounding inference. By then, students will know that checking assumptions and conditions is a fundamental part of doing inference

4)

The central limit theorem states that if you have a population with mean and standard deviation and take sufficiently large random samples from the ... If the population is normal, then the result holds for samples of any size i.e, the sampling distribution of the sample means will be approximately normal

The central limit theorem is perhaps the most fundamental result in all of statistics. It allows us to understand the behavior of estimates across repeated sampling and thereby conclude if a result from a given sample can be declared to be “statistically significant,” that is, different from some null hypothesized value.The central limit theorem is perhaps the most fundamental result in all of statistics. It allows us to understand the behavior of estimates across repeated sampling and thereby conclude if a result from a given sample can be declared to be “statistically significant,” that is, different from some null hypothesized value.

The Central Limit Theorem states that the sampling distribution of the sampling means approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. ... It's a useful phenomenon that can help accurately predict characteristics of a population.

5)

Increase the sample size. Often, the most practical way to decrease the margin of error is to increase the sample size. ...

Reducing variability. The less that your data varies, the more precisely you can estimate a population parameter. ...

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