This page shows you how to run the common tests in Minitab, which value in the Session output actually matters, and how to write the result in APA. When the deadline is tight or the data will not behave, a statistician can run and interpret it on your own worksheet.
Minitab is menu-driven, so the analysis is less about typing commands than knowing which menu to open and which value in the Session output to read. Pick the wrong test for the design, arrange the data in the wrong layout, or read the wrong column, and the answer is wrong no matter how tidy the output looks. This guide is part of our statistics homework help. It gives the exact menu path for each common test, points at the one value that decides the result, and shows how to report it. Where a test can fail, it names the check and the fallback test to use instead.
Two things that trip people up in Minitab. First, the 2-Sample t dialog asks whether your data is stacked (all values in one column with a second column of group labels) or unstacked (each group in its own column), and choosing the wrong option is the most common reason the test will not run. Second, Minitab prints the p-value in a column labelled P-Value, and the test statistic sits beside it, so read both, not one.
Match your research question and variable types to the right test, then jump to its guide.
| Your question | Your data | Test |
|---|---|---|
| Do two unrelated groups differ on average? | Continuous outcome, two independent groups | Independent-samples t-test |
| Did the same people change between two points? | Continuous outcome, two paired measures | Paired t-test |
| Do three or more unrelated groups differ? | Continuous outcome, three or more groups | One-way ANOVA |
| Are two categories associated? | Two categorical variables | Chi-square test of association |
| Do two continuous variables move together? | Two continuous variables | Pearson correlation |
Not sure your data meets the requirements? Every guide below lists the assumptions and the test to switch to when one fails.
Minitab opens with two panes. The Worksheet is the grid where your data lives, with columns down the page and rows across. The Session window is where every result appears as text after you run a command, so this is the pane you read and copy from. In current Minitab the output also shows in the Navigator on the left, but the numbers are the same.
Columns in the Worksheet are named C1, C2, C3 and so on. The shaded box directly under each column heading is for the variable name, for example Score or Group, and the numbered rows below it hold the values. You can type data straight in, or bring it in with File > Open for a Minitab worksheet or an Excel, CSV or text file. Give every column a clear name before you analyze anything, because the dialogs list columns by name and a blank C4 is easy to select by mistake.
Stacked versus unstacked data. This is the layout question that decides whether the 2-Sample t test and One-Way ANOVA will run. Stacked data puts every outcome value in one column, with a second column of group labels beside it, so one column holds the numbers and one holds the category. Unstacked data splits the outcome so each group sits in its own column. The 2-Sample t dialog asks which you have, and it is worth checking before you click OK. If your data is in the wrong shape, convert it with Data > Stack Columns or Data > Unstack Columns rather than retyping it.
The common tests live under one menu. T-tests, correlation and the basic descriptives sit under Stat > Basic Statistics, ANOVA sits under Stat > ANOVA, and the chi-square test of association sits under Stat > Tables.
Menu path, the assumption checks, the exact value to read in the Session output, and how to report it. Example values follow standard teaching datasets.
Compares the means of one continuous outcome across two unrelated groups, for example test scores for two teaching methods.
Assumptions. Continuous outcome, two independent groups, no serious outliers, and approximate normality within each group, which you can check with a normality plot under Stat > Basic Statistics > Normality Test. By default Minitab does not assume equal variances, which is the safe choice. If normality is badly broken, switch to the Mann-Whitney test under Stat > Nonparametrics > Mann-Whitney.
What to read. In the Session output, the Test block gives the T-Value, the DF and the P-Value. The estimation block above it gives each group mean and standard deviation for the write-up.
Common mistakes
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Compares two measurements taken on the same people, for example a score before and after training.
Assumptions. A continuous outcome measured twice on the same cases, no serious outliers, and normality of the difference scores, not the raw variables. Minitab works on the paired difference internally, so run the normality check on that difference. If the differences are badly non-normal, use the Wilcoxon signed-rank test under Stat > Nonparametrics > 1-Sample Wilcoxon on the differences.
What to read. The Test block gives the T-Value, the DF, which equals the number of pairs minus one, and the P-Value. Minitab prints a very small p-value as 0.000, which you report as p < .001, never p = .000, because a probability is never exactly zero.
Common mistakes
Compares the means of one continuous outcome across three or more unrelated groups.
Assumptions. Continuous outcome, three or more independent groups, no serious outliers, normality of the residuals, and roughly equal variances. Check normality and equal spread on the residual plots and with Stat > ANOVA > Test for Equal Variances. If the variances are clearly unequal, use the Welch option under Options, and if normality is badly broken use the Kruskal-Wallis test under Stat > Nonparametrics > Kruskal-Wallis.
What to read. The Analysis of Variance table gives the F-Value and the P-Value on the Factor row. Then read the Grouping Information table from Tukey: groups that do not share a letter differ significantly.
Common mistakes
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Tests whether two categorical variables are associated, for example gender and a preferred learning format.
Assumptions. Both variables categorical, independent observations, and expected counts large enough, with no more than 20 percent of cells below an expected count of five. Minitab flags cells with low expected counts under the table. If that rule fails, collapse sparse categories or use Fisher's exact test. For paired categorical data, for example the same people before and after, use McNemar's test instead.
What to read. The Chi-Square Test table, the Pearson row, for the Chi-Square value, the DF and the P-Value. Ignore the Likelihood Ratio row unless your course asks for it.
Common mistakes
Measures the strength and direction of the linear relationship between two continuous variables.
Assumptions. Two continuous variables, a linear relationship and no serious outliers, both checked on a scatterplot before you trust the coefficient, and approximate normality. Pearson only captures linear association, so a strong curved relationship can still return a small r. For ordinal data or a monotonic but non-linear relationship, use Spearman rho, which Minitab offers as the Spearman rho method in the same dialog.
What to read. The Session output gives the Pearson correlation, which is r from minus one to plus one, and the P-Value for the test that the true correlation is zero.
Common mistakes
Most Minitab marks are lost not on the software but on ignoring a broken assumption. Check the assumption, and when it fails, switch to the matched test rather than reporting an invalid result.
| Assumption | How Minitab checks it | If it fails |
|---|---|---|
| Normality (two groups) | Normality Test, or the residual plots | Use the Mann-Whitney test |
| Normality (paired) | Normality Test on the difference scores | Use the Wilcoxon signed-rank test |
| Normality (three or more groups) | Residual plots from the ANOVA | Use the Kruskal-Wallis test |
| Equal variances | Test for Equal Variances | Use the Welch option in One-Way ANOVA |
| Expected cell counts | Low-count flag under the table | Collapse categories or use Fisher's exact test |
| Linearity | Scatterplot | Use Spearman rho |
Procedures on this page were checked against the Minitab 2-Sample t support documentation and the Minitab One-Way ANOVA support documentation.
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Choose Stat > Basic Statistics > 2-Sample t. If your outcome and group labels sit in two columns, pick Both samples are in one column and set the Samples and Sample IDs columns. If each group has its own column, pick Each sample is in its own column. Read the T-Value, DF and P-Value in the Session output.
Stacked data keeps all of the outcome values in one column with a second column of group labels. Unstacked data splits the outcome so each group sits in its own column. The 2-Sample t dialog asks which layout you have, and choosing the wrong one is the most common reason the test will not run. Convert between them with Data > Stack Columns or Data > Unstack Columns.
Minitab labels it P-Value in the Session output, in the same row or table as the test statistic. For a t-test it sits beside the T-Value, for ANOVA beside the F-Value, and for a chi-square test beside the Chi-Square value. If P-Value is below your alpha level, usually 0.05, the result is statistically significant.
In Stat > ANOVA > One-Way, click Comparisons and tick Tukey. Minitab prints a Grouping Information table and the pairwise differences with adjusted p-values. Groups that do not share a letter in the Grouping column differ significantly.
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