You are testing a claim and incorrectly use the normal sampling distribution instead of the t-sampling distribution. Does this make it more or less likely to reject the null hypothesis? Is this result the same no matter whether the test is left-tailed, right-tailed, or two-tailed? Explain your reasoning. Is the null hypothesis more or less likely to be rejected? Explain. Less likely; for degrees of freedom less than 30, the tail of the curve are thicker for a t-sampling distribution. Therefore, if you incorrectly use a standard normal sampling distribution, the area under the curve at the tails will be larger than what it would be for the t-test, meaning the critical value(s) will lie closer to the mean. Is the result the same? The result is different. With a two-tailed case, the tail thickness does not affect the location of the critical values, however, in a left- and right-tailed case, the tail thickness does affect the location of the critical value. The result is different. With a left- and right-tailed case, the tail thickness does not affect the location of the critical value, however, in a two-tailed case, the tail thickness does affect the location of the critical value. The result is the same. In each case, the tail thickness affects the location of the critical value(s). The result the same. In each case, the tail thickness does not affect the location of the critical value(s).

If the sample size is sufficiently large, we can use normal distribution even when we don't know about the population variance. Hence, there is no change in the decision about the null hypothesis, since the critical values are approximately same. It is same for whether it is left-tailed, right-tailed or two tailed.

if the sample size is small and we don't know about the population variance, in that case normally assumption is fail so we cannot use the normal sampling distribution, we use only the t-sampling distribution. in this if we use the normal sapling distribution there is a difference in the inference about the null hypothesis

More likely for degrees of freedom less than 30, the tail of the curve are thicker for a t-sampling distribution. Therefore, if you incorrectly use a standard normal sampling distribution, the area under the curve t the tail will be smaller than what it would be for the t-test meaning the critical values will lie closer to the mean.

The result is the same, in each case, the tail thickness affects the location of the critical value.

Step-by-step explanation:

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step-by-step explanation:

you get texts and minutes, so why not

the answer is:

the answer is:

to solve the problem, we need to remember the quotient of power property, it's defined by the following relation:

if we have a quotienf of powers that have the same base, we need to keep the same base and subtract the exponent of the denominator power to the exponent of the numerator power.

so, we are given the expression:

then, calculating we have:

hence, the answer is:

have a nice day!