AP Statistics Part IV – Inference: Conclusions with Confidence Chapter 10: Introduction to Inference 10.1Estimating with Confidence To make an inference is to make an educated guess. The guess is made based on information that is collected and analyzed. Statistical inference provides us with methods for drawing conclusions about a population based on information gathered from a sample. Probability is to be used to determine just how strong our inference is. Normally in statistics, we stay about from using the word causes. We much prefer the safe word trending, or association. However, in true hypothesis testing, we are trying to conclude that the explanatory variable causes the response variable.

AP Statistics Part IV – Inference: Conclusions with Confidence

Chapter 10: Introduction to Inference 10.1Estimating with Confidence Homework: #’s 1, 3, 4, 5, 6, 8, 10, 12, 14, 18, 20, 21

AP Statistics Part IV – Inference: Conclusions with Confidence Chapter 10: Introduction to Inference 10.2Tests of Significance The goal of significance testing is to assess the strength of a claim against the population parameter. The reasoning, as is often the case in statistics, is to ask the question, “what would happen if we repeated the sample or experiment many times?” Since we are using sample statistics to make some claim about the population parameter, we must state our hypotheses based on the parameters.

AP Statistics Part IV – Inference: Conclusions with Confidence

Chapter 10: Introduction to Inference 10.2Tests of Significance Homework: 27, 30, 32, 35, 36, 40, 44

AP Statistics Part IV – Inference: Conclusions with Confidence Chapter 10: Introduction to Inference 10.3Using Significance Tests Many times with significance testing, we are changing the wording from strong association to possible causation. Consider the pharmaceutical industry. A new diet pill may be proven to CAUSE weight loss, or a new pills may cause an increase in metabolism. The cause can only be used if a hypothesis has been thoroughly tested and has undergone three phases of hypothesis testing 1.Randomization 2.Control 3.Replication

AP Statistics Part IV – Inference: Conclusions with Confidence Chapter 10: Introduction to Inference 10.3Using Significance Tests One also must keep in mind the ramifications of a test of significance. Are there any consequences to having statistically significant evidence. Who does it affect? How much will it cost? There are many factors to consider. What about ACT scores? These reasons must be considered when doing the following…

AP Statistics Part IV – Inference: Conclusions with Confidence

Chapter 10: Introduction to Inference 10.3Using Significance Tests Homework: 58 & 59

AP Statistics Part IV – Inference: Conclusions with Confidence

Chapter 10: Introduction to Inference 10.4Inference as Decision Type I and Type II Errors What about the alternative? What would happen if our test statistic does not fall in the rejection zone, but the actual parameter value does lie in the rejection zone? In other words, what if we test a sample & find the null hypothesis to be not worthy of rejection? We accept the sample, right? What if the actual population parameter produces a test statistic that is in the rejection zone? Haven’t we just accepted a bad population? Who is affected by this error and how often does this occur? This is known as a Type II Error? Which is worse?

AP Statistics Part IV – Inference: Conclusions with Confidence

Chapter 10: Introduction to Inference 10.4Inference as Decision Homework: #’s 66, 67, 70, & 71