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------------------------------------------------ Subject: Statistics 101

Discussion Instructions:

Please answer this discussion prompt. I'm having a hard time understanding it, so any clarification would be appreciated.

Discussion Topic: One Sample T-Test

This week's responses must relate to the one-sample t-test. Describe one hypothesis you'd like to test, ensuring it meets the criteria of a one-sample t-test. It's okay to use the same example as last week, as long as you specify how a t-test would be the appropriate analysis.

Consider the following questions:
- Are the population parameters (mean/standard deviation) unknown values?
- Where did the hypothesized values come from?
- Has previous research been conducted in this area, to your knowledge?
- What might be learned from studying this topic?

To receive full credit, this section must be at least 75 words.

Key Concepts:

This week, we are introduced to the "one-sample T-test," which tests if a given sample of observations could have been generated from a population with a specified mean. For this week's discussion assignment, describe a hypothesis that could be tested specifically with this type of analysis.

Important Distinctions:

A Z-test (Week 7, Ch. 8) and T-test (Week 8, Ch. 9) have separate uses and functions. One key difference is whether population parameters are known (Z-test) or unknown (T-test). With a T-test, a researcher can use the mean from a sample of scores to estimate whether a hypothesized population mean is significantly different or not. The researcher starts with a hypothesis about the population mean and calculates a sample mean and standard deviation to perform the statistical comparison. Just one sample is involved. Next week, we will look at "two-sample T-tests/Ch. 10," which allow for comparisons between two independent groups.

The one-sample t-test is a statistical analysis used to test if a given sample of observations could have been generated from a population with a specified mean. This test is appropriate when the population parameters are unknown, and it allows researchers to estimate whether a hypothesized population mean is significantly different from the sample mean.

An example hypothesis that could be tested using the one-sample t-test is as follows: "The average weight of newborn babies in a particular hospital is 7.5 pounds." In this case, the researcher is interested in testing whether the population mean weight of newborns is equal to 7.5 pounds.

To conduct the one-sample t-test, the researcher would collect a sample of newborn weights from the hospital. The mean and standard deviation of the sample would be calculated, along with the degrees of freedom. The researcher would then use these values to calculate the t-statistic and compare it to the critical value from the t-distribution at the desired level of significance.

If the calculated t-statistic falls within the critical region, the researcher would reject the null hypothesis and conclude that the average weight of newborn babies is significantly different from 7.5 pounds. On the other hand, if the calculated t-statistic falls outside the critical region, the researcher would fail to reject the null hypothesis and conclude that there is not enough evidence to suggest a significant difference in the average weight.

Through this analysis, the researcher can gain insights into the population parameter of interest (average weight of newborns) based on the sample data and draw conclusions about the hypothesis tested.

Learn more about weight here:

https://brainly.com/question/3081641

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