Large sample test for population mean

However, it is also possible that the blocks do meet the specification. The P-value of 0.1963 is greater than the significance level of 0.05, indicating that there is not enough evidence to reject the null hypothesis. So, in summary, the P-value for the test is 0.1963, indicating that there is not enough evidence to reject the null hypothesis and conclude that the blocks do not meet the specification.
  • #1
Poke

Homework Statement


[/B]
A new concrete mix is being designed to provide adequate compressive strength for concrete blocks. The specification for a particular application calls for the blocks to have a mean compressive strength μ greater than 1350 kPa. A sample of 100 blocks is produced and tested. Their mean compressive strength is 1356 kPa and their standard deviation is 70 kPa. A test is made of H0:μ ≤1350 versus H1:μ>1350.

a. Find the P-value.
b. Do you believe it is plausible that the blocks do not meet the specification, or are you convinced that they do? Explain your reasoning.

Homework Equations


It is asking for Population Mean, with large sample (n=100)
H0:μ ≤1350 ; H1:μ>1350 ; μ0 = 1350

Z = \frac{Mean - μ0}{ \frac{\sigma}{\sqrt{n}} }

The Attempt at a Solution


As H1:μ> μ0 , Area to the right of Z

Z = 0.8571, between 0.8023 and 0.8051
so P = 1 - \frac{0.8023+0.8051}{2}
P = 0.1963 > 0.05 (5%) = \alpha

So we cannot reject H0, and we can only conclude the blocks do not meet the specification is plausible.

Am I doing it in a correct approach, and final answer correct? Thanks!
 
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  • #2
It is possible, yes, you cannot exclude it.
 

What is a large sample test for population mean?

A large sample test for population mean is a statistical method used to determine whether a sample of data is representative of a larger population. It is typically used when the sample size is large enough to assume a normal distribution of the data.

What is the purpose of a large sample test for population mean?

The purpose of a large sample test for population mean is to make inferences about the true population mean based on a sample of data. It helps researchers to determine if the sample accurately reflects the population, and if the results can be generalized to the larger population.

How is a large sample test for population mean performed?

A large sample test for population mean involves calculating the sample mean and standard deviation, determining the appropriate test statistic (such as a t-test or z-test), and comparing the test statistic to a critical value based on the chosen significance level. If the test statistic is greater than the critical value, the result is considered statistically significant.

What are the assumptions of a large sample test for population mean?

The assumptions of a large sample test for population mean include a normal distribution of the data, a random sample, and a large enough sample size (typically at least 30 observations). If these assumptions are not met, alternative methods may need to be used.

What are the advantages of a large sample test for population mean?

The advantages of a large sample test for population mean include its ability to provide more accurate and reliable results compared to smaller sample sizes, its ability to detect smaller differences between groups, and its ability to make more generalizable conclusions about the larger population.

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