Answer :
In hypothesis testing for a population mean, we start by setting up the null and alternative hypotheses. The standard procedure is:
1. The null hypothesis, denoted by [tex]$H_0$[/tex], states that there is no difference or effect. In this case it asserts that the population mean is equal to the claimed value. Therefore, we write:
[tex]$$
H_0: \mu = 19500.
$$[/tex]
2. The alternative hypothesis, denoted by [tex]$H_1$[/tex], is a statement that contradicts the null hypothesis. Often, when detecting any difference regardless of direction, a two-sided (or not-equal-to) alternative is used. This is expressed as:
[tex]$$
H_1: \mu \neq 19500.
$$[/tex]
This setup represents a two-tailed test where any significant deviation from [tex]$\$[/tex]19500[tex]$ in either direction would lead us to question the claim that the population mean is $[/tex]\[tex]$19500$[/tex].
Thus, the correct pair of hypotheses is:
- Null hypothesis: [tex]$\mu = 19500$[/tex]
- Alternative hypothesis: [tex]$\mu \neq 19500$[/tex]
This corresponds to option 1 from the given choices.
1. The null hypothesis, denoted by [tex]$H_0$[/tex], states that there is no difference or effect. In this case it asserts that the population mean is equal to the claimed value. Therefore, we write:
[tex]$$
H_0: \mu = 19500.
$$[/tex]
2. The alternative hypothesis, denoted by [tex]$H_1$[/tex], is a statement that contradicts the null hypothesis. Often, when detecting any difference regardless of direction, a two-sided (or not-equal-to) alternative is used. This is expressed as:
[tex]$$
H_1: \mu \neq 19500.
$$[/tex]
This setup represents a two-tailed test where any significant deviation from [tex]$\$[/tex]19500[tex]$ in either direction would lead us to question the claim that the population mean is $[/tex]\[tex]$19500$[/tex].
Thus, the correct pair of hypotheses is:
- Null hypothesis: [tex]$\mu = 19500$[/tex]
- Alternative hypothesis: [tex]$\mu \neq 19500$[/tex]
This corresponds to option 1 from the given choices.