Learn how to use stability analysis for determining the shelf life of a product. Understand how to apply DOE for process improvement. Learn how to evaluate a random sample of product from a lot to determine whether to accept or reject the entire lot. Understand how to utilize important capability analysis tools to evaluate your processes relative to internal and customer specifications. Develop the necessary skills to successfully evaluate and certify measurement systems. ![]() Learn the foundation for important statistical concepts for determining if a process mean is off target, whether two means are significantly different, and for demonstrating if a process change does not significantly affect a critical response. Learn how to easily import data and export output. Develop sound statistical approaches to data analysis by understanding how to select the right tool for a given scenario and to correctly interpret the results of the analysis. The two-sample t-test (also known as the independent samples t-test) is a method used to test whether the unknown population means of two groups are equal or not.Learn to apply Minitab tools commonly used in the pharmaceutical industry. ![]() Yes, a two-sample t-test is used to analyze the results from A/B tests. You can use the test when your data values are independent, are randomly sampled from two normal populations and the two independent groups have equal variances. Analysis of variance (ANOVA) is one such method. What if the variances for my two groups are not equal? Other multiple comparison methods include the Tukey-Kramer test of all pairwise differences, analysis of means (ANOM) to compare group means to the overall mean or Dunnett’s test to compare each group mean to a control mean. What if my data isn’t nearly normally distributed? You use a different estimate of the standard deviation. If your sample sizes are very small, you might not be able to test for normality. You might need to rely on your understanding of the data. When you cannot safely assume normality, you can perform a nonparametric test that doesn’t assume normality.įor the two-sample t-test, we need two variables. The second variable is the measurement of interest. We have students who speak English as their first language and students who do not.We also have an idea, or hypothesis, that the means of the underlying populations for the two groups are different. ![]() Our two groups are the native English speakers and the non-native speakers. Our idea is that the mean test scores for the underlying populations of native and non-native English speakers are not the same. We measure the grams of protein in two different brands of energy bars.We want to know if the mean score for the population of native English speakers is different from the people who learned English as a second language. Our measurement is the grams of protein for each energy bar. Our idea is that the mean grams of protein for the underlying populations for the two brands may be different. We want to know if we have evidence that the mean grams of protein for the two brands of energy bars is different or not. The variances for the two independent groups are equal.įor very small groups of data, it can be hard to test these requirements.Data in each group are normally distributed.Data in each group must be obtained via a random sample from the population.Measurements for one observation do not affect measurements for any other observation. Below, we'll discuss how to check the requirements using software and what to do when a requirement isn’t met.
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