Calculate the test statistic. Here, we'll be using the formula below for the general form of the test statistic.In later lessons you'll learn more objective assumptions. The null and alternative hypotheses will always be written in terms of population parameters the null hypothesis will always contain the equality (i.e., \(=\)). In this lesson we'll be confirming that the sampling distribution is approximately normal by visually examining the sampling distribution. Check assumptions and write hypotheses. The assumptions will vary depending on the test.This is slightly different from the five step procedure that we used when conducting randomization tests. In the remaining lessons, we will use the following five step hypothesis testing procedure. This method is preferred by many because z scores are on a standard scale (i.e., mean of 0 and standard deviation of 1) which makes interpreting results more straight forward.ĭrag the slider at the bottom of the graph to see normal curve fit on the randomization plot.ħ.4.1 - Hypothesis Testing 7.4.1 - Hypothesis Testing In practice, when we construct confidence intervals and conduct hypothesis tests we often use the normal distribution (or t distributions which you'll see next week) as opposed to bootstrapping or randomization procedures in situations when the sampling distribution is approximately normal. Essentially, it is determined by the point at which the sampling distribution becomes approximately normal. Over the next few lessons we will examine what constitutes a "sufficiently large" sample size. The Central Limit Theorem states that if the sample size is sufficiently large then the sampling distribution will be approximately normally distributed for many frequently tested statistics, such as those that we have been working with in this course: one sample mean, one sample proportion, difference in two means, difference in two proportions, the slope of a simple linear regression model, and Pearson's r correlation. For example, we can use Minitab to find the z values that offset the middle 90% of the z distribution, which would be the multipliers for a 90% confidence interval.Īs we saw at the beginning of this lesson, many of the sampling distributions that you have constructed and worked with this semester are approximately normally distributed. In Lesson 4, we used the standard error method to construct a 95% confidence interval by estimating the z* multiplier to be 2 using the Empirical Rule, because approximately 95% of a normal distribution falls within two standard deviations of the mean. Later in this lesson, we'll see that the procedures we're learning here, specifically finding the z scores that offset the middle X%, can be used to determine the z* multiplier to construct a confidence interval for any confidence level.
How to use minitab 18 how to#
In this lesson, we'll learn how to find such values on the z distribution or on a normal distribution with a given mean and standard deviation.
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This can be used to find the value that offset a given proportion, such as the top 10%, bottom 25%, or middle 95%. Minitab can also be used to find the values that separate a given proportion of the normal distribution.