1. State a quantity or proportion Find the area to the left of the score The shaded area is the probability of observing a quantity or proportion less than or equal to the stated quantity or proportion. Calculate the score Probability of Observing a Quantity or Proportion ProportionQuantity Calculate the score

2. State the hypothesis p-value approach: The probability of the null hypothesis being true is the p-value. Significance approach: If p-value < significance level, reject the null hypothesis. State the conclusion Calculate the score Hypothesis Test for a Single Proportion Find the p-value (standard normal distribution) H A ≠ p-value = 1 – norm.s.dist(z-score, TRUE) p-value = norm.s.dist(z-score, TRUE) p-value = 2 x (1 – norm.s.dist(abs(z-score), TRUE)) H A proportion > kH A proportion < k

3. State the hypothesis p-value approach: The probability of the null hypothesis being true is the p-value. Significance approach: If p-value < significance level, reject the null hypothesis. State the conclusion Calculate the score Hypothesis Test for Comparing Proportions Find the p-value (standard normal distribution) H A <H A ≠H A > p-value = 1 – norm.s.dist(z-score, TRUE) p-value = norm.s.dist(z-score, TRUE) p-value = 2 x (1 – norm.s.dist(abs(z-score), TRUE))

4. State the hypothesis p-value approach: The probability of the null hypothesis being true is the p-value. Significance approach: If p-value < significance level, reject the null hypothesis. State the conclusion Calculate the score Hypothesis Test for a Single Mean Find the p-value (t distribution) t H A ≠ p-value = 1 – t.dist(t-score, df, TRUE) p-value = t.dist(t-score, df, TRUE) p-value = 2 x (1 – t.dist(abs(t-score), df, TRUE)) H A mean > kH A mean < k

5. State the hypothesis p-value approach: The probability of the null hypothesis being true is the p-value. Significance approach: If p-value < significance level, reject the null hypothesis. State the conclusion Calculate the score Hypothesis Test for Comparing Means Find the p-value (t distribution) t H A >H A <H A ≠ p-value = 1 – t.dist(t-score, df, TRUE) p-value = t.dist(t-score, df, TRUE) p-value = 2 x (1 – t.dist(abs(t-score), df, TRUE))

6. State the hypothesis H A ≠ p-value approach: The probability of the null hypothesis being true is the p-value. Significance approach: If p-value < significance level, reject the null hypothesis. State the conclusion Calculate the score Hypothesis Test for a Single Variance Find the p-value (Chi-squared distribution) p-value = 1 – chi.dist(x-score, df, TRUE) p-value = chi.dist(x-score, df, TRUE) p-value =2 x (1 – chi.dist(x-score, df, TRUE)) or 2 x chi.dist(x-score, df, TRUE), whichever is less H A variance > kH A variance < k

7. State the hypothesis H A larger > smallerH A larger < smallerH A ≠ State the conclusion Calculate the score Hypothesis Test for Comparing Variances Find the p-value (F distribution) p-value = 1 – f.dist(f-score, df, df, TRUE) p-value = f.dist(f-score, df, df, TRUE) p-value =2 x (1 – f.dist(f-score, df, df, TRUE)) p-value approach: The probability of the null hypothesis being true is the p-value. Significance approach: If p-value < significance level, reject the null hypothesis.