t-tests
The two sample t-test is commonly used to compare the means of two groups. This tutorial explains what a t-test is, how the F-test decides between Student’s and Welch’s, and how to run the whole analysis in RAISINS… Read more …
The t-test is one of the most widely used statistical methods for comparing the means of two independent groups to determine whether they differ significantly. It is applied throughout agricultural, biological, medical, and social science research to evaluate treatment effects and group differences. This tutorial begins from first principles: what a t-test actually does, how the one sample, two sample, and paired versions differ, and why RAISINS runs an F-test before choosing between Student’s and Welch’s t-test. It then walks through the complete analysis in RAISINS, including summary statistics, variance homogeneity, t-test results, normality assessment, publication-ready tables and plots, and AI-assisted interpretation, all without writing a single line of code.
1 What is a t-test?
Suppose you grow a new rice variety in fifteen plots and the old variety in fifteen others. The new variety averages 103.9 kg per plot, the old one 76.0 kg. The new variety looks better. But does it?
Here is the difficulty. If you had grown the same variety in all thirty plots, the two sets of fifteen would still not average exactly the same. Soil varies. Water varies. Some plots simply get lucky. So a difference of 27.9 kg could mean one of two things, and you cannot tell which just by looking:
- The new variety really does yield more.
- The two varieties are identical, and this gap is just the ordinary noise you would get from any thirty plots.
A t-test is the tool that decides between these two explanations. It does so by asking one deceptively simple question:
Is the gap between the two averages big compared to the noise inside each group?
That is the whole idea. If the groups are tight and well-behaved, even a small gap is convincing. If the groups are scattered all over the place, even a large gap might be an accident. The t-test formalises this comparison into a single number, the t-statistic:
\[t = \frac{\text{difference between the two means}}{\text{how much the data naturally wobble}}\]
A big t means the signal beats the noise. A small t means it does not.
A t-test compares the difference between two averages against the natural variability of the data, and tells you whether the difference is too large to be explained by chance alone.
1.1 The p-value, in plain language
Once RAISINS computes t, it converts it into a p-value, which answers this question:
If the two groups were genuinely identical, how often would I see a difference this big purely by luck?
If the answer is “about 40% of the time” (p = 0.40), the difference is unremarkable and you learn nothing. If the answer is “less than once in a thousand tries” (p < 0.001), then either something extraordinary just happened, or the two groups are genuinely different. Researchers conventionally draw the line at p < 0.05: less than a 1-in-20 chance of being a fluke is treated as evidence of a real difference.
The p-value is not the probability that your hypothesis is correct. It is the probability of seeing your data if there were no real difference. A small p-value says the data would be surprising under “no difference”, so “no difference” becomes hard to believe.
1.2 The null and alternative hypotheses
Every t-test starts by assuming nothing is happening. This starting assumption is the null hypothesis (H₀), and the test then looks for evidence against it.
- H₀ : μ₁ = μ₂: the two population means are equal (nothing is going on).
- H₁ : μ₁ ≠ μ₂: the two population means differ (something is going on).
A small p-value means the data are hard to reconcile with H₀, so you reject it. A large p-value means the data are perfectly compatible with H₀, so you fail to reject it. Note the careful wording: failing to reject H₀ is not proof that the groups are identical. It only means you did not find enough evidence to say otherwise. Absence of evidence is not evidence of absence.
A little history: beer, small samples, and a man called "Student"
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The t-test was born in a brewery. William Sealy Gosset, an English chemist and statistician, joined the Guinness Brewery in Dublin in 1899, where he faced a very practical problem: he needed to draw reliable conclusions about barley varieties and brewing processes from tiny samples. The statistical theory of the day assumed large samples, which Gosset simply did not have. In 1908 he worked out the distribution that a sample mean actually follows when the sample is small and the population standard deviation is unknown, and this became the t-distribution. Guinness prohibited its employees from publishing, fearing trade secrets would leak, so Gosset published under the pseudonym “Student”, which is why we still call it Student’s t-test today. Ronald A. Fisher later extended and formalised the method, and it went on to become a cornerstone of agricultural trials, medical research, and the life sciences for more than a century.
2 One sample, two sample, or paired?
RAISINS offers three t-test modules, and choosing correctly matters more than any other decision in this tutorial. The arithmetic is similar in all three; what changes is where the two numbers you are comparing come from. Figure 1 lays them side by side.
| Test | What you compare | Typical question |
|---|---|---|
| One sample t-test | One group against a known or claimed value | Is the protein content of our variety different from the advertised 7.5%? |
| Two sample t-test | Two separate, independent groups | Does the Treatment plot yield more than the Control plot? |
| Paired t-test | The same individuals, measured twice | Did the same 15 plants grow taller after fertiliser was applied? |
If the same plants, animals, or patients are measured before and after something, you must use the paired t-test, not the two sample test. Treating paired data as independent throws away the very information that makes the comparison sensitive, and often hides a real effect. The rule of thumb: can you draw a line from each observation in group 1 to its partner in group 2? If yes, it is paired.
The rest of this tutorial deals with the two sample t-test: two independent groups, different individuals in each.
3 How RAISINS chooses your test
Here is where most textbooks lose people, so let us go slowly.
There is not one two sample t-test. There are two versions, and they differ in one respect: whether the two groups are assumed to have the same spread (variance).
- Student’s t-test assumes the two groups wobble by roughly the same amount. Because it can assume this, it pools the variability of both groups into a single, better estimate. This makes it slightly more powerful.
- Welch’s t-test makes no such assumption. It is safer when the spreads differ, at the cost of a small amount of power.
So how do you know which spread situation you are in? You test for it. That test is the F-test.
The F-test compares the two groups’ variances by dividing one by the other:
\[F = \frac{s_1^2}{s_2^2} \quad \text{(larger variance on top)}\]
If both groups wobble by the same amount, this ratio should land near 1. The further it strays from 1, the more the spreads disagree. Crucially, the F-test says nothing whatsoever about the means. It is purely about spread. It is a gatekeeper, not the main event.
Putting it together: RAISINS first checks normality, then runs the F-test on the spreads, then picks Student’s or Welch’s accordingly, and only then reports whether the means differ. Figure 2 shows the whole decision as a single picture.
Reading the F-test result: a worked mental example
Suppose you compare Treatment and Control yields.
- F = 1.18, p = 0.76 → the ratio is close to 1, and a p-value of 0.76 means a ratio this far from 1 would happen 76% of the time by chance alone. The spreads are compatible. Student’s t-test is used.
- F = 4.90, p = 0.004 → the ratio is far from 1, and such a result would occur only 4 times in 1000 if the spreads truly matched. The spreads genuinely differ. Welch’s t-test is used.
Note the direction of the logic, which trips up almost everyone at first: for the F-test, a large p-value is the “good” outcome (it lets you use the more powerful pooled test). For the t-test, a small p-value is the interesting outcome (it means your groups genuinely differ). Same threshold, opposite meaning, because the two tests have opposite null hypotheses of interest.
RAISINS runs the F-test, applies the correct t-test automatically, and reports which one it used in a dedicated Test Used column. The flowchart is there so you understand why the software chose what it chose, not because you must choose yourself.
4 Assumptions of the two sample t-test
Every statistical test buys its conclusions on credit, and the assumptions are the terms of the loan. The two sample t-test asks for four things:
| Assumption | What it means | What if it fails? |
|---|---|---|
| Independence | Each observation is unrelated to the others; no plot influences its neighbour | Serious. No fix at the analysis stage, this must be handled by good experimental design |
| Normality | Values within each group follow a roughly bell-shaped distribution | Try a transformation (Section 14); if it persists, use the Mann–Whitney U test |
| Equal variance | The two groups wobble by similar amounts | RAISINS handles this for you by switching to Welch’s t-test |
| Continuous response | The measurement is a proper number (yield, height, weight), not a category or rank | Use a test designed for that data type instead |
Independence is the assumption you truly cannot compromise on, and it is settled long before analysis, when you design the experiment and randomise. Normality matters most in small samples; once each group exceeds about 30 observations, the Central Limit Theorem ensures the means behave normally even when the raw data do not. Equal variance is handled automatically. So in practice, design well and RAISINS takes care of the rest.
5 Getting to the module
Now that the theory is clear, let us run the test. Visit the RAISINS home page at www.raisins.live and go to Data Analysis. In this tutorial we use the Two Sample t-test module, shown in Figure 3.
5.1 Citing RAISINS, and the record behind this module
Before you run anything, it is worth knowing what you can point a reviewer to afterwards.
To cite the platform itself in a paper, thesis, or report, use the RAISINS citation, available in APA, Harvard, and BibTeX formats at www.raisins.live/citation.html. That is the primary reference, and for most manuscripts it is all you need.
Alongside it, each module has a CPRR, short for Computational Provenance & Reproducibility Record. It answers a fair question any reviewer is entitled to ask: what exactly did the software do to my data? Point-and-click tools often keep their workings hidden. RAISINS does the opposite. The record for this module states the R version and the exact version of every package used, names the specific function behind each reported result (stats::var.test() for the F-test, stats::t.test() for the comparison of means, and stats::shapiro.test(), nortest::ad.test() and tseries::jarque.bera.test() for normality), lists every default parameter and decision rule the module applies, and includes runnable R code reproducing each analytical step on a public dataset. It carries its own DOI.
The record for the two sample t-test is at www.raisins.live/module_record/ttest.html.
Cite the RAISINS paper as your primary reference for the platform. Add the CPRR as supporting documentation when a journal asks for details of the computing environment, or when you want your methods section to be precise about versions and functions rather than saying “analysis was carried out using an online tool.” The CPRR supports the citation; it does not replace it.
6 Preview mode and Quick Tour
Before subscribing, you can explore the entire module using Preview mode, accessible from the Welcome page. Preview mode loads built-in datasets so you can try every feature (analysis, normality tests, plots, and the RA-One assistant) without uploading your own data. First-time users are also offered a Quick Tour, an interactive, step-by-step walkthrough that highlights each control and explains what it does. You can retake the tour at any time from the Quick Tour tab.
7 A working example
This example compares two independent groups, Treatment and Control, with 15 observations each. For every experimental unit, three response variables were recorded (Height, Weight, and Yield), illustrating how RAISINS can analyse several variables in a single run. Each variable is compared between the two groups independently, producing its own t-test result. The Treatment group shows higher values than the Control group across all three variables, suggesting a possible treatment effect. The task now is to determine whether those differences are large enough, relative to the noise, to be taken seriously.
8 How to prepare your data
Your analysis is only as good as your data. Feed RAISINS high-quality data and it will deliver powerful insights; feed it messy data and the results will not be trustworthy. You have Four routes:
- Create your dataset in MS Excel
- Build your dataset directly within the RAISINS app
- Using the Model datasets in RAISINS as a reference
- Create your dataset using the RA-One chat assistant
9 Preparing data in MS Excel
Open a new blank sheet in MS Excel containing only one sheet, and avoid adding any unnecessary content. The dataset should follow a column-based format, where the first column holds the group label for the two independent groups being compared (e.g. “Treatment” and “Control”). All response variables under study (e.g. Height, Weight, Yield) should occupy separate columns, and each group label repeats according to the number of observations in that group (15 per group in this example). The file can be saved as CSV, XLS, or XLSX, but CSV is recommended as it is lighter and loads faster. Ensure there are no unwanted spaces in column names or group labels. For reference, see the structure in Figure 5; Figure 4 shows the same arrangement with group labels repeated per replicate.
Dataset creation rules
- Column naming convention
- No spaces allowed in column names.
- Use underscores (
_) or full stops (.) for separation. - Avoid symbols and special characters such as %, #.
- Data arrangement
- Start the data towards the upper-left corner.
- Ensure the row above the data is not blank.
- Cell management
- Avoid typing or deleting in cells without data.
- If needed, select the affected cells, right-click, and choose Clear Contents.
- Column relevance
- Name all columns meaningfully.
- Exclude unnecessary columns not required for the analysis.
- Group labels
- The group column must contain exactly two distinct labels corresponding to the two groups being compared.
- Keep spelling and capitalisation of group labels consistent throughout the column.
How to save as CSV in MS Excel
Open your workbook. Ensure your data is arranged properly with only one sheet.
Click the ‘File’ menu. Go to the top-left corner and click File.
Choose ‘Save As’ or ‘Save a Copy’. Select the location where you want to save your file.
Set file type to CSV. In the ‘Save as type’ dropdown, choose CSV (Comma delimited) (*.csv).
Name your file. Enter a relevant file name without spaces (use underscores if needed).
Click ‘Save’. Click Save to export the file.
💡 Tip: Before saving, double-check that your data is on the first sheet and follows the required format: no empty rows above the data, meaningful column names, and exactly two unique group labels in the group column.
10 Prepare using Create Data in RAISINS
If you are unsure about the correct format, do not worry, RAISINS can create the data layout for you using the prescribed template. Here is how:
- Navigate to the Create Data tab
- Select the number of Observations(Replications) in each group
- Select the number of Variables
- Click the Create button
The model layout appears as shown in Figure 6. You may enter the observations manually into the CSV file once downloaded, or paste them straight into the file provided. Once the observations are entered, download the CSV and upload it under Analysis.
11 Download Model Datasets
If you are unsure about the required data format or would like to explore the module before using your own data, RAISINS provides model datasets for reference. To download them:
- Navigate to the Datasets tab
- Click the Download CSV link corresponding to the required dataset
- Save the file to your computer
- Use the model dataset as a reference for preparing your own data or upload it directly to explore the analysis
12 Creating a dataset using RA-One chat
RA-One, the built-in chat assistant, can help you create a properly formatted dataset through a simple conversation. To get started, open the RA-One chat by clicking the chat icon available within the app or by heading over to the RA-One tab. Now you can enter the number of observations(replications), and response variables, and RA-One will generate a dataset in the required format. You can review the generated dataset in the chat, download it as a CSV file, and upload it directly under the Analysis tab. The full workflow is illustrated in Figure 8 below.
13 The Analysis tab
Figure 9 shows the Analysis tab in detail, with each option explained. Upload your prepared file by clicking Browse in the sidebar. Once uploaded, selectors for the Group column and the response Variables appear. Choose the appropriate column under Groups, then select the variables you wish to analyse. You can also switch between a Paired and an Unpaired (independent) t-test depending on your design. Refer back to Section 2 if you are unsure which applies.
Click Run Analysis and all outputs appear instantly across the sub-tabs: Analysis Results, Normality Test, Plots & Graphs, Interpretation, FAQs, and View Data. On the Analysis Results panel you can further adjust the alternative hypothesis (two-tailed, or one-tailed with Mean 1 < Mean 2 or Mean 1 > Mean 2), the significance level (α), the number of decimal digits, and the font. If your variables turn out not to be normally distributed, RAISINS provides a built-in transformation option (Section 14).
One-tailed or two-tailed? Choose before you look at the data
A two-tailed test asks whether the two means differ in either direction. This is the default and the right choice in nearly all research, because it keeps you honest: you are prepared to be surprised in either direction.
A one-tailed test asks whether Mean 1 is specifically greater than (or specifically less than) Mean 2. It is more powerful, but only legitimate when a difference in the opposite direction would be meaningless or impossible to you, and when you committed to that direction before seeing your results. Switching to a one-tailed test after noticing which group came out ahead is a well-known way to manufacture significance that is not there. When in doubt, stay two-tailed.
14 Transformation
Log, square root, and arcsine transformations are used to make data more normal and to even out uneven variation. You can apply them directly in RAISINS as shown in Figure 10.
Logarithmic transformation converts a skewed distribution into a more symmetrical one by replacing each data point (x) with its logarithm. It is applied to positive, continuous data where the variance grows in proportion to the mean, a pattern common in phenomena that grow multiplicatively or exponentially.
Square root transformation stabilises variance and reduces right-skewness by replacing each data point (x) with its square root. It is primarily used for non-negative count data, such as those following a Poisson distribution, where variance increases with the mean. By compressing the upper end of the scale more than the lower end, it brings the data closer to normality.
Arcsine transformation (the angular transformation) is designed for proportions or percentages bounded between 0 and 1. By taking the inverse sine of the square root of the proportion, it stretches the ends of the distribution near 0 and 1, where variance is naturally small. It is chiefly used to achieve homoscedasticity in binomial data.
After choosing the appropriate transformation, proceed to Section 15 for the analysis.
15 Analysis results
Once your dataset is uploaded and you click Run Analysis, the two sample t-test is performed for every selected variable. Three tables appear in the Analysis Results sub-tab: Summary Statistics, the F-test for homogeneity of variance, and the t-test result. They should be read in that order, exactly following the flowchart in Figure 2.
Table 1: Summary statistics
For each group and each selected variable, RAISINS reports the sample size (N), Mean, Standard Deviation (SD), the five-number summary (Minimum, Q1, Median, Q3, Maximum), and the Coefficient of Variation (CV %). In the working example the Treatment group has a higher mean than the Control group for all three variables (Height 13.53 vs 7.43, Weight 26.54 vs 18.67, Yield 103.87 vs 76.00), while the low CV values (roughly 6–16%) show that variability within each group is modest. This is the “signal” and the “noise” from Section 1, side by side, and the signal already looks comfortably larger.
Table 2: Test for homogeneity of variance (F-test)
This is the gatekeeper step from Section 3. If the F-test p-value is ≥ 0.05 the variances are treated as homogeneous and the pooled Student’s t-test is used; if p < 0.05 the variances differ and Welch’s t-test is applied automatically. A short note beneath the table states the decision for each variable. In the working example the variances are homogeneous for all three variables (Height F = 1.87, p = 0.25; Weight F = 1.31, p = 0.62; Yield F = 1.18, p = 0.76), so Student’s t-test was used in every case.
Table 3: t-test result
How the t-test result table is built
The null hypothesis states that the means of the two groups are equal (H₀: μ₁ = μ₂), and the alternative states that they differ (H₁: μ₁ ≠ μ₂). The test statistic t is computed by dividing the difference between the two sample means by the standard error of that difference. Under the null hypothesis this statistic follows a t-distribution, with degrees of freedom determined either by the pooled method (equal variances) or by Welch’s approximation (unequal variances). The computed t-value is compared against a critical t-value at the chosen significance level (α = 0.05 or 0.01). If the absolute computed t-value exceeds the critical value, or equivalently if the p-value is less than α, the null hypothesis is rejected, indicating a statistically significant difference between the two group means.
Significance is indicated using asterisks: a single asterisk ( * ) for the 10% level, two asterisks ( ** ) for the 5% level, and three asterisks ( *** ) for the 1% level, while NS denotes a non-significant result. These markers appear in the Significance column of the table.
RAISINS automatically performs both Student’s t-test (assuming equal variances) and Welch’s t-test (not assuming equal variances), and presents both, so you can see the result under either assumption while the Test Used column tells you which one the F-test selected.
15.1 Interpretation from Figure 13
The results show that the Treatment group differs significantly from the Control group for all three variables. For Height, the Treatment mean (13.53) is much higher than the Control mean (7.43), with a t-statistic of 11.91 on 28 degrees of freedom and a p-value below 0.001. For Weight, the Treatment mean (26.54) exceeds the Control mean (18.67), with t = 13.76, df = 28, and p < 0.001. For Yield, the Treatment mean (103.87) is well above the Control mean (76.00), with t = 13.51, df = 28, and p < 0.001. In every case the F-test found the variances homogeneous (p > 0.05), so the pooled Student’s t-test was applied, and all three p-values are significant at the 1% level (marked ***).
Read that against Section 1: a t-statistic near 12 means the gap between the groups is roughly twelve times larger than the noise in the data. Differences of that magnitude are highly unlikely to have arisen by chance, so we reject the null hypothesis and conclude that Treatment and Control genuinely differ in Height, Weight, and Yield. Had any p-value exceeded 0.05, the null hypothesis could not be rejected and those two group means would be considered statistically similar.
A tiny difference can be statistically significant if your sample is large enough, and a large, agronomically valuable difference can miss significance in a small trial. Always read the mean difference and its confidence interval alongside the p-value, and ask whether the size of the effect matters in the field, not just on the page.
A quick glossary of the result columns
Overview of t-test results and interpretation
- Groups and Response Variables
Groups: The two independent categories (e.g. Treatment and Control) whose means are being compared.
Response Variable: The dependent variable or specific measurement (e.g. Height, Weight, Yield) recorded to evaluate the performance of the two groups. RAISINS can test several response variables at once, reporting a separate t-test for each.
- Test Statistics
t-statistic: The computed test value that quantifies the standardised difference between the two group means relative to the variability in the data.
Degrees of Freedom (df): Determines the shape of the t-distribution used to compute the p-value. In the pooled case, df = n₁ + n₂ − 2; in Welch’s approximation, df is adjusted based on the sample variances.
p-value: The probability that the observed difference in means (or a larger one) would arise by chance if the null hypothesis were true. A p-value below 0.05 is conventionally considered statistically significant.
- Group Descriptive Statistics
For each group, RAISINS reports the sample size (N), Mean, Standard Deviation (SD), the five-number summary (Minimum, Q1, Median, Q3, Maximum), and the Coefficient of Variation (CV %), giving a complete picture of central tendency, spread, and relative variability.
- Mean Difference, Confidence Interval, and Test Used
Mean difference: The difference between the two group means. The table also reports the confidence interval for this difference (at the selected confidence level) and a Test Used column indicating whether Student’s or Welch’s t-test was applied, based on the F-test for equality of variances.
16 Normality test and plots
Why test for normality?
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The two sample t-test is a parametric test that assumes the response variable is approximately normally distributed within each group. When this assumption is violated, particularly in small samples, the t-test may produce unreliable p-values. Normality testing helps you decide whether the parametric t-test is appropriate, or whether a non-parametric alternative such as the Mann–Whitney U test should be used instead. For large samples (n > 30 per group), the Central Limit Theorem generally ensures that the sampling distribution of the mean is approximately normal even if the raw data are not, reducing the need for formal normality testing.
Under the Normality Test sub-tab, RAISINS reports four formal normality tests for each selected variable (the Shapiro–Wilk test, the Anderson–Darling test, the Lilliefors (Kolmogorov–Smirnov) test, and the Jarque–Bera test) together with an Overall decision that summarises them. Use the variable selector to switch between variables.
The Shapiro–Wilk test is the most widely used, being one of the most powerful tests for small to moderate samples; its statistic W ranges from 0 to 1, with values close to 1 indicating normality. A significant result (p < 0.05) indicates a departure from normality. Alongside the table, a pair of fully customisable Q–Q plots, one for Treatment and one for Control, lets you assess normality visually within each group (see Figure 14).
A Q–Q plot ranks your observations and plots them against the values you would expect if the data were perfectly normal. If the assumption holds, the points fall close to the straight diagonal line. Gentle wandering is normal and harmless. A clear curve, an S-shape, or points flying away at the ends signals a real departure. For small samples the Q–Q plot is often more informative than the formal tests, which lack the power to detect much of anything when n is small.
Normality test details
Shapiro–Wilk Test
The Shapiro–Wilk test evaluates whether a sample comes from a normally distributed population. It computes a statistic W by comparing the observed data quantiles with the expected quantiles of a normal distribution. The null hypothesis is that the data are normally distributed; a significant p-value (p < 0.05) leads to rejection. It is most reliable for sample sizes between 3 and 50, and is considered more powerful than alternatives such as Kolmogorov–Smirnov for small samples.
\[W = \frac{\left(\sum_{i=1}^{n} a_i x_{(i)}\right)^2}{\sum_{i=1}^{n}(x_i - \bar{x})^2}\]
where \(x_{(i)}\) are the order statistics (sorted values) and \(a_i\) are constants derived from the expected values of normal order statistics.
Anderson–Darling Test
A goodness-of-fit test that gives extra weight to the tails of the distribution, making it especially sensitive to departures from normality in the extremes.
Lilliefors (Kolmogorov–Smirnov) Test
An adaptation of the Kolmogorov–Smirnov test for the case where the mean and variance are estimated from the data. It compares the empirical cumulative distribution of the sample with that of a normal distribution.
Jarque–Bera Test
A test based on the sample skewness and kurtosis that assesses whether they match those expected under a normal distribution.
Overall decision
RAISINS combines the four tests into a single Overall verdict for the variable. Because this table is computed on the variable as a whole (both groups combined), a clear difference between the two group means can make the pooled data look non-normal even when each group is individually normal. This is why the per-group Q–Q plots are the more reliable visual check.
Which test to use based on normality?
The choice depends on whether the normality assumption is satisfied. When the data are approximately normal within each group, the two sample t-test (Student’s or Welch’s, depending on the F-test) is appropriate. If a variable is genuinely non-normal within groups, first attempt a logarithmic or square root transformation (Section 14); if it remains non-normal after transformation, the Mann–Whitney U test, a non-parametric alternative, is recommended. This is exactly the left-hand branch of Figure 2.
17 Interpretation
RAISINS provides a clear and concise interpretation of your t-test results to help you understand the statistical findings with ease. The interpretation summarizes the key results, indicates whether a statistically significant difference exists between the groups, explains the test used (Student’s or Welch’s, where applicable), and presents the findings in a publication-ready format. You can access this from the Interpretation sub-tab of the Analysis tab (Figure 15).
18 RA-One Chat
RA-One is the built-in conversational assistant for the t-tests module, available from the RA-One tab. You ask questions in plain language and it answers using your own analysis rather than generic statistical advice. Every result it discusses is drawn from what the module actually computed - it never invents numbers, and if a value isn’t available it says so instead of guessing. All answers are in plain English, with no code or software commands.
RA-One works directly with your Analysis Results, Normality Test, and Interpretation outputs. It can explain what a p-value or confidence interval means for your specific groups and variables, walk you through why Student’s or Welch’s t-test applies to your data, and interpret the Shapiro–Wilk / Anderson–Darling normality results. It also handles general concept questions - means, standard deviation, when variances are pooled, and how to read the interval - so you can build understanding alongside your results.
The same chat window can also prepare your data. It can build a correctly formatted dataset template (Section 12) for you to fill in, or fetch a model dataset (Section 11) so you can try the module straight away - so you never need to leave the tab to get a file ready.
RA-One can also generate plots on request. Ask for a boxplot comparing the two groups, a box plot of group means, or a Q–Q plot for a variable, and the app renders the graphic directly in the chat, where you can view it and refine it by asking for changes.
Within a single conversation, RA-One can interpret your results, build a data template, fetch a model dataset, and produce plots - so most of a routine t-test session can be conducted without ever leaving the chat window.
19 FAQs
The module includes a dedicated FAQs section to clarify common doubts and guide you through the features. It offers detailed answers, additional information, and helpful tips for a smooth experience. If you are ever unsure how something works, say which test to use when data are not normally distributed, or how to choose between Student’s and Welch’s t-test, the FAQs are a good place to start.
20 View data
View Data is the primary diagnostic tool for ensuring data integrity before analysis. When you upload your dataset, the system performs an automated Health Check to validate column types and formatting. For the two sample t-test this step is especially important: it confirms that the group column contains exactly two unique labels, that all response variable columns are numeric, and that there are no missing or badly formatted entries that could distort the results.
21 Wrapping up
The two sample t-test rests on one honest question: is the gap between these two averages larger than the noise? Everything else, the F-test, the Welch correction, the normality checks, exists only to make sure that question is answered fairly. RAISINS automates the machinery so you can concentrate on what the answer means for your research.
If your data do not fit the two sample design, the companion modules are there: the one sample t-test when comparing against a known standard, and the paired t-test when the same units are measured twice. And if you get stuck at any point, RA-One is available 24 × 7, or write to us at [email protected].