**05 t-TEST, 1-SAMPLE
**In the fifth Minitab tutorial, we will accompany the heat treatment process of the skateboard axles and see how the so-called hypothesis test, t-test for one sample, can be used to find out whether the heat treatment process is set so that the skateboard axles achieve the required compressive strength. To achieve this, the skateboard axles undergo a multi-stage heat treatment process. Our task in this Minitab tutorial is, to use sample data and the hypothesis test t-test for one sample, to make a reliable recommendation to the production management, as to whether the current heat treatment process is sufficiently well adjusted, or whether the current process might even need to be stopped and optimized if our hypothesis test shows, that the required mean target value is not being achieved. In the core of this Minitab tutorial, we will experience how a hypothesis test is properly carried out on a sample, and check in advance whether our data set follows the laws of normal distribution. With the help of a so-called discriminatory power analysis, we will work out whether the sample size is large enough. By using the so-called density function and probability distribution plot, we will learn how to classify the t-value in the t-test, and also understand which wrong decisions are possible in the context of a hypothesis test. With the help of corresponding individual value plot, we will develop an understand the so-called confidence interval, in the context of the one-sample t-test.

MAIN TOPICS MINITAB TUTORIAL 05, part 1

- t-test, one sample, fundamentals
- Retrieving and interpreting worksheet information
- Retrieving and interpreting descriptive statistics
- The discriminatory power of a hypothesis test
- Anderson- Darling test as a preliminary stage to the t-test
- Derivation of the probability plot based on the density function
- Interpretation of the probability plot
- Performance of the hypothesis test t-test for the mean value, 1 sample
- Establishing the null hypothesis and alternative hypothesis
- Test for normal distribution according to Anderson-Darling
- The probability plot of the normal distribution

MAIN TOPICS MINITAB TUTORIAL 05, part 2

- Generation and interpretation of individual value plot as part of the t-test
- Confidence level and probability of error
- Test sample size and significance value
- Type 1 error and type 2 error in the context of the hypothesis decision

MAIN TOPICS MINITAB TUTORIAL 05, part 3

- Power analysis and sample size in the t-test
- Influence of the sample size on the hypothesis result
- Graphical construction of the probability distribution
- Interpretation of the power curve
- Determination of the sample size based on the discrimination quality
- Influence of different sample sizes on the hypothesis decision