This Self-Learning Module (SLM) is prepared so that you, our dear learners, can continue your studies and learn while at home. Activities, questions, directions, exercises, and discussions are carefully stated for you to understand each lesson.
Each SLM is composed of different parts. Each part shall guide you step-by-step as you discover and understand the lesson prepared for you.
Pre-tests are provided to measure your prior knowledge on lessons in each SLM. This will tell you if you need to proceed on completing this module or if you need to ask your facilitator or your teacher’s assistance for better understanding of the lesson. At the end of each module, you need to answer the post-test to self-check your learning. Answer keys are provided for each activity and test. We trust that you will be honest in using these.
Please use this module with care. Do not put unnecessary marks on any part of this SLM. Use a separate sheet of paper in answering the exercises and tests. And read the instructions carefully before performing each task.
In the previous module, you have learned to identify the appropriate test statistic when the population variance is known or unknown. You were able to define different statistical concepts related to z-test and t-test as the tools for computing value in hypothesis testing problem. The steps in choosing correct statistical test were also discussed. Moreover, the test used for Central Limit Theorem was explained.
Since you already know how to choose the test statistic applicable in hypothesis testing, you are now ready to identify the appropriate rejection region when population variance is known or unknown. In determining rejection region, you will also be defining other statistical concepts such as critical value.
After going through this module, you are expected to:
1. define the critical values, level of significance, hypothesis test, and rejection region;
2. identify the critical value when population variance is known or unknown; and
3. determine the appropriate rejection region for a given level of significance when population is known/unknown and Central Limit Test is to be used.