Chloe's question via email about a p-value

In summary, a hypothesis test is being conducted where the null hypothesis is that the mean is equal to 13, and the alternative hypothesis is that the mean is less than 13. The given parameters are a mean of 13, a standard deviation of 2.47, a sample mean of 12.86, and a sample size of 20. The test statistic is calculated to be approximately -0.253481, leading to a p-value of 0.40129. However, a more accurate value can be obtained using technology, which gives a p-value of 0.399948. Overall, the approximation is very close to the actual value.
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I'm assuming the hypothesis test is

$\displaystyle H_0 : \mu = 13 \quad \quad H_a : \mu < 13 $

We are given $\displaystyle \mu = 13, \quad \sigma = 2.47, \quad \bar{x} = 12.86 , \quad n = 20 $.

The test statistic is

$\displaystyle \begin{align*} z &= \frac{\bar{x} - \mu}{\frac{\sigma}{\sqrt{n}}} \\
&= \frac{12.86 - 13}{\frac{2.47}{\sqrt{20}}} \\
&\approx -0.253\,481 \end{align*} $

Thus the p value is

$\displaystyle \begin{align*} p &= \textrm{Pr}\left( Z < -0.253\,481 \right) \\
&\approx \Phi \left( -0.25 \right) \textrm{ from the Z distribution table}\\
&= 0.401\,29 \end{align*} $

However, a CAS (or Linear Interpolation) could be used to get a more accurate value. Using technology, I find the p value to be $\displaystyle 0.399\,948 $, which our approximation is very close to.
 
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Nice work as usual.

Thanks
Bill
 

FAQ: Chloe's question via email about a p-value

What is a p-value?

A p-value is a statistical measure that helps determine the significance of a hypothesis test. It represents the probability of obtaining results at least as extreme as the observed results, assuming the null hypothesis is true.

Why is a p-value important in scientific research?

A p-value is important because it allows researchers to determine if their results are statistically significant. This helps determine if the results are due to chance or if there is a true relationship between the variables being studied.

How do you interpret a p-value?

A p-value can be interpreted as the likelihood of obtaining the observed results if the null hypothesis is true. A smaller p-value indicates stronger evidence against the null hypothesis, while a larger p-value suggests that the results are more likely due to chance.

What factors can influence the p-value?

The p-value can be influenced by sample size, effect size, and the chosen level of significance. A larger sample size and a larger effect size can lead to a smaller p-value, while a smaller level of significance can result in a larger p-value.

How is a p-value calculated?

The p-value is calculated by comparing the observed results to the expected results under the null hypothesis. This is typically done using statistical software or tables, which determine the probability of obtaining results as extreme as the observed results.

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