However, there are possible solutions to correct such violations (e.g., transforming your data) such that you can still use a one-sample t-test. In fact, do not be surprised if your data violates one or more of these assumptions. You have to check that your data meets these assumptions because if it does not, the results you get when running a one-sample t-test might not be valid. This is more of a study design issue than something you can test for, but it is an important assumption of the one-sample t-test.Īssumptions #3 and #4 relate to the nature of your data and can be checked using Minitab. Assumption #2: The data are independent (i.e., not correlated/related), which means that there is no relationship between the observations.If you are unsure whether your dependent variable is continuous (i.e., measured at the interval or ratio level), see our Types of Variable guide. Examples of continuous variables include height (measured in feet and inches), temperature (measured in ☌), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), firm size (measured in terms of the number of employees), age (measured in years), reaction time (measured in milliseconds), grip strength (measured in kg), power output (measured in watts), test performance (measured from 0 to 100), sales (measured in number of transactions per month), academic achievement (measured in terms of GMAT score), and so forth. Assumption #1: Your dependent variable should be measured at a continuous level (i.e., it is an interval or ratio variable).Assumptions #1 and #2 are explained below: If these assumptions are not met, there is likely to be a different statistical test that you can use instead. However, you should check whether your study meets these two assumptions before moving on. You cannot test the first two of these assumptions with Minitab because they relate to your study design and choice of variables. Minitab AssumptionsĪ one-sample t-test has four assumptions. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a one-sample t-test to give you a valid result.
In this guide, we show you how to carry out a one-sample t-test using Minitab, as well as interpret and report the results from this test. The lecturer could use a one-sample t-test to compare the weekly study time of a sample of 20 students to the suggested 10 hours. Alternately, imagine that a lecturer believed her course required 10 hours of study time per week and wanted to determine whether students spent this amount of time studying.
The academic could use a one-sample t-test to compare the GMAT score of the 100 participants against the national average. This population mean is not always known, but is sometimes hypothesized.įor example, imagine that an academic was conducting research on the relationship between exam performance and revision time, but wanted to first check whether his 100 participants reflected the national average in terms of their academic ability, measured in terms of their GMAT score. The one-sample t-test is used to determine whether a sample comes from a population with a specific mean. KATHLEEN ZGONC 1949 - 2004 Loyal servants and good friends of Thiel College and of the Greenville, Pennsylvania community.One-sample t-test using Minitab Introduction This document is dedicated to the memories of
71 LESSON 17 - REGRESSION AND CORRELATION. LESSON 15 - HYPOTHESIS TESTING INDEPENDENT SAMPLES. 61 LESSON 14 - HYPOTHESIS TESTING DEPENDENT SAMPLES. 54 LESSON 13 -HYPOTHESIS TESTING SMALL SAMPLE. 51 LESSON 12 - HYPOTHESIS TESTING LARGE SAMPLE. 38 CREATING A PROBABILITY TABLE FOR X ~ B(n, p). 24 LESSON 5 - MEASURES OF CENTRAL TENDENCY. 17 TABULATED NOMINAL VARIABLE (FREQUENCY TABLE).
17 UNTABULATED NOMINAL VARIABLE (RAW DATA). 8 INSTRUCTIONS FOR ALL MINITAB ASSIGNMENTS.
Fourth Edition Revised for Minitab Version 14 and Windows XP by