What do you infer about the difference between two population means? Inferences about Two Means with Independent Samples (Assuming Unequal Variances) Using independent samples means that there is no relationship between the groups. These populations

## What do you infer about the difference between two population means?

Inferences about Two Means with Independent Samples (Assuming Unequal Variances) Using independent samples means that there is no relationship between the groups. These populations are not related, and the samples are independent. We look at the difference of the independent means.

## When making inferences about the difference in population means with the 2 sample t test the observed value for the test statistic is?

For the null hypothesis, the observed t-statistic is equal to the difference between the two sample means divided by the standard error of the difference between the sample means.

**When comparing proportions from two populations we do inference about?**

In Inference for Two Proportions, we learned two inference procedures to draw conclusions about a difference between two population proportions (or about a treatment effect): (1) a confidence interval when our goal is to estimate the difference and (2) a hypothesis test when our goal is to test a claim about the …

**What inferences on the mean of a population do we have?**

Making inferences about a population mean requires several assumptions: 1) Random: The data come from a random sample of size n from the population of interest or a randomized experiment. 2) Normal: The population has a Normal distribution.

### Which test is used to compare the mean of two populations?

two-sample t-test

The two-sample t-test (Snedecor and Cochran, 1989) is used to determine if two population means are equal. A common application is to test if a new process or treatment is superior to a current process or treatment. There are several variations on this test. The data may either be paired or not paired.

### What are the conditions of inference in statistics?

The conditions we need for inference on one proportion are: Random: The data needs to come from a random sample or randomized experiment. Normal: The sampling distribution of p^p, with, hat, on top needs to be approximately normal — needs at least 10 expected successes and 10 expected failures.

**What is statistical inference Why is it important quizlet?**

Inferential statistics does allow us to make conclusions beyond the data we have to the population to which it was drawn. Inference: The process of drawing conclusions about population parameters based on a sample taken from the population. From the data we can find a conclusions about the population.

**What are the two major components of inference?**

There are two forms of statistical inference:

- Hypothesis testing.
- Confidence interval estimation.

## When do we use sample proportion to make an inference?

When we use a sample proportion to make an inference about a population proportion, there is uncertainty. For this reason, inference involves probability. Under certain conditions, we can model the variability in sample proportions with a normal curve. We use the normal curve to make probability-based decisions about population values.

## Which is a hypothesis test regarding two population proportions?

Therefore, Population 2 is the group of people who have at least some college education and consume too much cholesterol. First, let’s perform a hypothesis test on the difference in the two population proportions using a level of significance α = 0.05 (i.e., 5%).

**Can a population be the same as a population 2?**

Now if both Population 1 and Population 2 are the same in terms of the required proportion, they could be considered to be the “same” population. (Think about this a bit.)

**Which is the most challenging part of inference?**

The most challenging part of using inference tools on problems dealing with two populations is keeping the information straight. Before starting any of the inference methods, take a few moments to label each population. That way you won’t get confused about which information pertains to which population.