Computing the test statistic and p-value in minitab express

First, the defendant is guilty (Reject the null hypothesis). There are two possible conclusions that the jury can reach. The prosecuting attorney tries to prove that the defendant is guilty (Alternative hypothesis-guilty). A defendant is innocent until proven guilty (Null hypothesis-innocent). This means that you do NOT have enough evidence to support the alternative claim (H 1).ДЄnother way to think about hypothesis testing is to compare it to the US justice system. Option 2) Fail to reject the null hypothesis (H 0). This means that you have enough statistical evidence to support the alternative claim (H 1).

Option 1) Reject the null hypothesis (H 0). The conclusion is made up of two parts:ДЎ) Reject or fail to reject the null hypothesis, and 2) there is or is not enough evidence to support the alternative claim. We are merely saying that the sample evidence is not strong enough to warrant the rejection of the null hypothesis. It is important to realize that we never prove or accept the null hypothesis. The conclusion must always be clearly stated, communicating the decision based on the components of the test. The conclusion is the final decision of the hypothesis test. The level of significance ( О±) is the probability that the test statistic will fall into the critical region when the null hypothesis is true. It is defined by the level of significance. The critical value is the value that defines the rejection zone (the test statistic values that would lead to rejection of the null hypothesis). It is compared to the level of significance ( О±). The p-value is the area under the curve to the left or right of the test statistic. It is used to decide whether the difference between the sample statistic and the hypothesized claim is significant. The test statistic converts the sample mean ( xМ„) or sample proportion ( pМ‚) to a Z- or t-score under the assumption that the null hypothesis is true. The test statistic is a value computed from the sample data that is used in making a decision about the rejection of the null hypothesis. It contains the value of the parameter that we consider plausible and is denoted as H 1. The alternative hypothesis is the claim to be tested, the opposite of the null hypothesis. It contains the condition of equality and is denoted as H 0 (H-naught).

The null hypothesis is a statement about the value of a population parameter, such as the population mean (Вµ) or the population proportion ( p). We believe that a treatment has helped, and we want to test the claim that there has been a reduction in the proportion of infected plants. Two years ago, the proportion of infected plants was 37%. A hypothesis test is a way for us to use our sample statistics to test a specific claim. Hypothesis testing is a procedure, based on sample evidence and probability, used to test claims regarding a characteristic of a population.ДЄ hypothesis is a claim or statement about a characteristic of a population of interest to us. Should you reject the known or accepted information in favor of your results? How confident are you in your estimate? At what point would you say that there is enough evidence to reject the known information and support your alternative claim? How far from the known mean of four years can the sample mean be before we reject the idea that the average lifespan of a hummingbird is four years? You have been studying the hummingbirds in the southeastern United States and find a sample mean lifespan of 4.8 years.

Use sample statistics to make inferences about population parameters.ДЇor example, past research tells us that the average life span for a hummingbird is about four years.

Quantify how confident we are about our claim.

This is the basic idea behind a hypothesis test: We want to test whether this claim is believable. When conducting scientific research, typically there is some known information, perhaps from some past work or from a long accepted idea. This chapter introduces the next major topic of inferential statistics: hypothesis testing.ДЄ hypothesis is a statement or claim about a property of a population. The previous two chapters introduced methods for organizing and summarizing sample data, and using sample statistics to estimate population parameters.

Computing the test statistic and p-value in minitab express