Final answer:
In hypothesis testing, we compare the sample statistics to the population parameters to test a hypothesis. A teacher tests if 85% of students want to go to a zoo and an instructor tests if the average test score is over 65. Statistical calculators aid in these tests by computing critical values and p-values.
Step-by-step explanation:
Understanding Hypothesis Testing
When conducting a hypothesis test, we compare a sample statistic to a population parameter to decide whether there is enough evidence to reject a null hypothesis.
In the example provided, a teacher conducts a hypothesis test to determine if the percentage of students who want to go on a field trip is different from 85%. Using the specified level of significance, the hypothesis test involves calculating a test statistic and comparing it to a critical value or using a p-value to make a decision.
In another case, an instructor hypothesizes that the mean score of a test is greater than 65, based on sample data. This right-tailed test at a 5 percent level of significance also uses the concept of a test statistic and p-value to reach a conclusion.
Additionally, predictive statistics can be used to estimate certain outcomes, such as final exam scores, based on other correlated variables, as long as the sample data falls within the domain of observed values.
The process of hypothesis testing can be aided by the use of statistical software or calculators, which compute critical values, test statistics, and p-values efficiently, aiding in making informed decisions.