To calculate the test statistic, we need the following information, depending on the type of test being conducted (e.g., $t$-test, $z$-test, etc.):

1. Type of Test: Is it a one-sample $t$-test, two-sample $t$-test, paired $t$-test, or another kind?
2. Sample Mean ($\bar{x}$): The mean of the sample data.
3. Population Mean ($\mu$): The claimed or hypothesized population mean.
4. Sample Standard Deviation ($s$): The standard deviation of the sample.
5. Sample Size ($n$): The number of observations in the sample.
6. Significance Level ($\alpha$): If provided, this will help evaluate the test statistic.

The general formula for a $t$-test statistic is:

$t = \frac{\bar{x} - \mu}{s / \sqrt{n}}$

If you provide these details, I can help compute the test statistic and round it to two decimal places.

Would you like to share additional information for this calculation? If you have values in a document or image, feel free to upload it for review.

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Related Questions:

1. What is the difference between a $t$-test and a $z$-test in hypothesis testing?
2. How is the test statistic used to determine if we reject or fail to reject the null hypothesis?
3. What are degrees of freedom in a $t$-test, and how do they affect the calculation?
4. How do you interpret the $p$-value associated with a test statistic?
5. What is the effect of sample size on the test statistic?

Tip: Always ensure your sample data meets the assumptions for the statistical test you're using. For a $t$-test, the data should be approximately normally distributed for small samples.