Inference for Quantitative Data: Means · ⁨Inferensi untuk Data Kuantitatif: Rata-rata⁩

• VAR-7.A.1 When $s$ is used instead of $\sigma$ to calculate a test statistic, the corresponding distribution, known as the $t$-distribution, varies from the normal distribution in shape, in that more of the area is allocated to the tails of the density curve than in a normal distribution.
• VAR-7.A.2 As the degrees of freedom increase, the area in the tails of a $t$-distribution decreases.

• UNC-4.O.1 Because $\sigma$ is typically not known for distributions of quantitative variables, the appropriate confidence interval procedure for estimating the population mean of one quantitative variable for one sample is a one-sample $t$-interval for a mean.
• UNC-4.O.2 For one quantitative variable, $X$, that is normally distributed, the distribution of $t = \dfrac{(\overline{x} - \mu)}{\frac{s}{\sqrt{n}}}$ is a $t$-distribution with $n-1$ degrees of freedom.
• UNC-4.O.3 Matched pairs can be thought of as one sample of pairs. Once differences between pairs of values are found, inference for confidence intervals proceeds as for a population mean.

• UNC-4.P.1 In order to calculate confidence intervals to estimate a population mean, we must check for independence and that the sampling distribution is approximately normal:
  • a. To check for independence:
    • i. Data should be collected using a random sample or a randomized experiment.
    • ii. When sampling without replacement, check that $n \leq 10\%N$, where $N$ is the size of the population.
  • b. To check that the sampling distribution of $\overline{x}$ is approximately normal (shape):
    • i. If the observed distribution is skewed, $n$ should be greater than 30.
    • ii. If the sample size is less than 30, the distribution of the sample data should be free from strong skewness and outliers.

• UNC-4.Q.1 The critical value $t^*$ with $n-1$ degrees of freedom can be found using a table or computer-generated output.
• UNC-4.Q.2 The standard error for a sample mean is given by $SE = \dfrac{s}{\sqrt{n}}$, where $s$ is the sample standard deviation.
• UNC-4.Q.3 For a one-sample $t$-interval for a mean, the margin of error is the critical value ($t^*$) times the standard error ($SE$), which equals $t^*\left(\dfrac{s}{\sqrt{n}}\right)$.

• UNC-4.R.1 The point estimate for a population mean is the sample mean, $\overline{x}$.
• UNC-4.R.2 For the population mean for one sample with unknown population standard deviation, the confidence interval is $\overline{x} \pm t^* \dfrac{s}{\sqrt{n}}$.

Boundary statement: Formulas for interval estimates do not appear explicitly on the AP Statistics Formula Sheet provided with the AP Statistics Exam. However, these formulas do not need to be memorized, as they can be constructed based on the general test statistic formula and the relevant standard error formulas that are provided on the formula sheet.

• UNC-4.V.1 Consider a simple random sample from population 1 of size $n_1$, mean $\mu_1$, and standard deviation $\sigma_1$ and a second simple random sample from population 2 of size $n_2$, mean $\mu_2$, and standard deviation $\sigma_2$. If the distributions of populations 1 and 2 are normal or if both $n_1$ and $n_2$ are greater than 30, then the sampling distribution of the difference of means, $\overline{x}_1 - \overline{x}_2$ is also normal. The mean for the sampling distribution of $\overline{x}_1 - \overline{x}_2$ is $\mu_1 - \mu_2$. The standard deviation of $\overline{x}_1 - \overline{x}_2$ is $\sqrt{\dfrac{(\sigma_1)^2}{n_1} + \dfrac{(\sigma_2)^2}{n_2}}$.
• UNC-4.V.2 The appropriate confidence interval procedure for one quantitative variable for two independent samples is a two-sample $t$-interval for a difference between population means.

• UNC-4.W.1 In order to calculate confidence intervals to estimate a difference of population means, we must check for independence and that the sampling distribution is approximately normal:
  • a. To check for independence:
    • i. Data should be collected using two independent, random samples or a randomized experiment.
    • ii. When sampling without replacement, check that $n_1 \leq 10\%N_1$ and $n_2 \leq 10\%N_2$.
  • b. To check that the sampling distribution of $(\overline{x}_1 - \overline{x}_2)$ should be approximately normal (shape):
    • i. If the observed distributions are skewed, both $n_1$ and $n_2$ should be greater than 30.

• UNC-4.X.1 For the difference of two sample means, the margin of error is the critical value ($t^*$) times the standard error ($SE$) of the difference of two means.
• UNC-4.X.2 The standard error for the difference in two sample means with sample standard deviations, $s_1$ and $s_2$, is $\sqrt{\dfrac{(s_1)^2}{n_1} + \dfrac{(s_2)^2}{n_2}}$.

• UNC-4.Y.1 The point estimate for the difference of two population means is the difference in sample means, $\overline{x}_1 - \overline{x}_2$.
• UNC-4.Y.2 For a difference of two population means where the population standard deviations are not known, the confidence interval is $(\overline{x}_1 - \overline{x}_2) \pm t^* \sqrt{\dfrac{s_1^2}{n_1} + \dfrac{s_2^2}{n_2}}$ where $\pm t^*$ are the critical values for the central C% of a $t$-distribution with appropriate degrees of freedom that can be found using technology.

Boundary statement: Formulas for interval estimates do not appear explicitly on the AP Statistics Formula Sheet provided with the AP Statistics Exam. However, these formulas do not need to be memorized, as they can be constructed based on the general test statistic formula and the relevant standard error formulas that are provided on the formula sheet.

• VAR-7.F.1 For a quantitative variable, the appropriate test for a difference of two population means is a two-sample $t$-test for a difference of two population means.

• VAR-7.G.1 The null hypothesis for a two-sample $t$-test for a difference of two population means, $\mu_1$ and $\mu_2$, is: $H_0 : \mu_1 - \mu_2 = 0$, or $H_0 : \mu_1 = \mu_2$. The alternative hypothesis is $H_a : \mu_1 - \mu_2 < 0$, or $H_a : \mu_1 - \mu_2 > 0$, or $H_a : \mu_1 - \mu_2 \neq 0$, or $H_a : \mu_1 > \mu_2$, or $H_a : \mu_1 < \mu_2$, or $H_a : \mu_1 \neq \mu_2$.

• VAR-7.H.1 In order to make statistical inferences when testing a difference between population means, we must check for independence and that the sampling distribution is approximately normal:
  • a. Individual observations should be independent:
    • i. Data should be collected using simple random samples or a randomized experiment.
    • ii. When sampling without replacement, check that $n_1 \leq 10\%N_1$ and $n_2 \leq 10\%N_2$.
  • b. The sampling distribution of $\overline{x}_1 - \overline{x}_2$ should be approximately normal (shape).
    • i. If the observed distribution is skewed, both $n_1$ and $n_2$ should be greater than 30.
    • ii. If the sample size is less than 30, the distribution of the sample data should be free from strong skewness and outliers. This should be checked for BOTH samples.

• VAR-7.I.1 For a single quantitative variable, data collected using independent random samples or a randomized experiment from two populations, each of which can be modeled with a normal distribution, the sampling distribution of $t = \dfrac{(\overline{x}_1 - \overline{x}_2) - (\mu_1 - \mu_2)}{\sqrt{\dfrac{s_1^2}{n_1} + \dfrac{s_2^2}{n_2}}}$ is an approximate $t$-distribution with degrees of freedom that can be found using technology. The degrees of freedom fall between the smaller of $n_1 - 1$ and $n_2 - 1$ and $n_1 + n_2 - 2$.
  • Illustrative examples for VAR-7.I.1: In a study comparing mean recovery times for two surgical procedures to repair a torn anterior cruciate ligament (ACL), the group receiving one procedure had a sample size of 110, while the group receiving the other procedure had a sample size of 100. The degrees of freedom fall between 100 (the smaller of 110 and 100) and 208 (110 + 100 - 2). The degrees of freedom may be determined using technology. If the test statistic for this study is $t \approx 7.13$, then the $p$-value is the area greater than 7.13 for a $t$-distribution with $df = 207.18$ (2018 FRQ 4).

Boundary statement: The formulas for test statistics do not appear explicitly on the AP Statistics Formula Sheet provided with the AP Statistics Exam. However, these formulas do not need to be memorized, as they can be constructed based on the general test statistic formula and the standard error formulas for each of the relevant test statistics that are provided on the formula sheet.

• DAT-3.G.1 An interpretation of the $p$-value of a significance test for a two-sample difference of population means should recognize that the $p$-value is computed by assuming that the null hypothesis is true, i.e., by assuming that the true population means are equal to each other.

• DAT-3.H.1 A formal decision explicitly compares the $p$-value to the significance $\alpha$. If the $p$-value $\leq \alpha$, then reject the null hypothesis, $H_0 : \mu_1 - \mu_2 = 0$, or $H_0 : \mu_1 = \mu_2$. If the $p$-value $> \alpha$, then fail to reject the null hypothesis.
• DAT-3.H.2 The results of a significance test for a two-sample test for a difference between two population means can serve as the statistical reasoning to support the answer to a research question about the populations that were sampled.