To determine if a value lies in the rejection region, we need to:

1. Identify the rejection region: This is defined by the hypothesis test you are performing and the level of significance ($\alpha$) you are using. For instance:

   • If it is a one-tailed test, the rejection region lies in one tail of the distribution.
   • If it is a two-tailed test, the rejection region lies in both tails of the distribution.

2. Calculate or compare the critical value(s): Based on the significance level and the test type, determine the critical value(s). For example:

   • A standard normal distribution with $\alpha = 0.05$ might have a critical $z$-value of $\pm 1.96$ for a two-tailed test.

3. Check the test statistic: If the test statistic (e.g., $z$-score, $t$-score) falls within the rejection region (e.g., greater than the upper critical value or less than the lower critical value), it lies in the rejection region.

If you share the specific values (test statistic, critical value, or distribution type), I can calculate this precisely for you.

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Do you want help calculating the rejection region or confirming the result? Here are five questions to clarify and expand the discussion:

1. What is the significance level ($\alpha$) of the test?
2. Is it a one-tailed or two-tailed test?
3. What is the test statistic value you are evaluating?
4. What distribution are you using (e.g., standard normal, $t$-distribution)?
5. Do you need to calculate the critical value(s) for the rejection region?

Tip: Always sketch the distribution and mark the critical value(s) to visualize the rejection region—it helps in understanding!