Final answer:
Row percentages are more appropriate for examining differences in hospitalization or death by blood type. To perform a hypothesis test to check if a sample is representative of national data, a chi-squared test is used and the null hypothesis is rejected if the p-value is less than the significance level.
Step-by-step explanation:
To determine whether the proportion of hospitalization or death differed by blood type, using row percentages would be more useful. This is because row percentages allow comparison within each blood type category, reflecting the proportion of outcomes (hospitalization/death) relative to the total number of individuals with that blood type. Without the specific data, the calculation of row percentages would involve taking the count of hospitalization or death within a blood type and dividing by the total count of that blood type, then multiplying by 100 to get a percentage.
To conduct a hypothesis test at the 5 percent significance level to confirm if survey participants are representative of the national statistics, we would first state the null hypothesis that there is no significant difference between the sample and the national data. We would then calculate the expected frequencies for each age group based on national percentages, compare them with the observed frequencies from the sample using a chi-squared test, and determine the p-value. If the p-value is less than 0.05, we would reject the null hypothesis, indicating that the survey participants are not representative of the national data.