How to Approach the Independent Samples T-Test Assessment
Five sections. Each one builds on the last. The DAA template looks straightforward until you realize each step has specific language, specific outputs, and a specific logic sequence the grader is checking. This guide walks you through how to approach each section so you do not lose points on things that are entirely avoidable.
The goal of this assessment is to figure out whether belonging to a particular group — in this case, attending a review session versus not attending — is associated with different mean final exam scores. That is a group mean comparison question. The statistical tool for that is the independent samples t-test. Each section of the DAA template has a specific job. Understanding what that job is before you start writing is what separates clean, high-scoring submissions from ones that get sent back for revision.
What This Guide Covers
What the Assessment Is Testing
This is not just a "run the test and paste the output" assignment. The DAA template requires you to demonstrate that you understand why you chose the test, how you checked its assumptions, what the results mean statistically, and how the same approach could apply in a real professional context. Each section tests a different layer of that understanding.
Students who open JASP first, run outputs, and then try to reverse-engineer the template sections almost always miss something. The template tells you exactly what each section needs. Read it completely. Then open the grades.jasp file. Running the analysis before you understand what each section is asking for creates unnecessary rework.
Understanding the Variables Before You Write Anything
Before writing a single word of Section 1, you need to be clear on two things: what each variable actually measures, and how to correctly classify it. Getting this wrong in Section 1 creates errors that ripple through every subsequent section.
Review — The Independent Variable
Review records whether a student attended a review session. The values are 1 (did not attend) and 2 (attended). These are labels, not quantities. You cannot calculate a meaningful average of them. That is the defining feature of a categorical variable — it puts cases into groups, it does not measure an amount. In the context of the t-test, Review is the grouping variable.
- Type: Categorical (nominal)
- Role in the analysis: Independent variable
- Groups defined: Group 1 = did not attend; Group 2 = attended
Final — The Dependent Variable
Final records the number of correct answers on the final exam. This is a count with meaningful differences between values — a student who scored 70 answered 10 more questions correctly than one who scored 60. That is a continuous variable. It is also the outcome we are trying to understand — making it the dependent variable in the analysis.
- Type: Continuous (ratio)
- Role in the analysis: Dependent variable
- What it measures: number of correct answers on the final exam
Section 1: How to Approach the Data Analysis Plan
Section 1 does three things: identifies the variables and their measurement types, states the research question, and sets up the hypotheses. None of this requires JASP. It is conceptual work that you should complete before running any analysis.
Name the Variables and Their Types
State each variable by name, define what it measures based on the data set instructions, and identify whether it is categorical or continuous. Do not just say "Review is categorical" — briefly explain why. Something like: Review categorizes students into groups based on attendance, so it is categorical. Final measures a count with meaningful numeric differences, so it is continuous. One to two sentences per variable is enough.
Write the Research Question
The research question frames the comparison you are making. It should ask whether there is a significant difference in the dependent variable between the two groups defined by the independent variable. Keep it as one clear sentence. Avoid vague phrasing like "explore the relationship" — this is a difference question, not a relationship question. That distinction matters for justifying the t-test later.
State the Null and Alternative Hypotheses
The null hypothesis (H₀) always states no difference — that the group means are equal. The alternative hypothesis (H₁ or Hᵃ) states that a significant difference exists. Keep them parallel in structure. What the null asserts about no difference, the alternative should directly contradict. Write them as declarative statements, not questions. The hypotheses establish the decision framework for everything in Sections 2, 3, and 4.
The independent samples t-test is appropriate when you have one categorical grouping variable and one continuous outcome variable, and you want to compare the means of the two groups. Your research question should make this structure explicit. If your question asks about a relationship or correlation instead of a group difference, your test choice would be different. Making the question precise here is not just academic — it is the justification for every methodological choice that follows.
Section 2: How to Approach Testing Assumptions (Levene’s Test)
The independent samples t-test has a key assumption: that the two groups have approximately equal variances (homogeneity of variances). Levene’s test checks this directly. The result of that test determines which version of the t-test you run in Section 3. You do not get to skip this step.
The p-Value of Levene’s Test Tells You Which T-Test Version to Use
This is the most important logical step in the entire assessment. It is also the one most students execute mechanically without understanding the underlying reasoning.
If Levene’s p > .05 (not significant): The assumption of equal variances is not violated. The two groups have similar enough variance that the standard Student’s independent samples t-test is appropriate. Proceed with Student’s t-test in Section 3.If Levene’s p < .05 (significant): The assumption is violated. The groups have significantly different variances. The Welch’s t-test, which does not assume equal variances, should be used instead. Switch to Welch’s in Section 3.
How to write the interpretation: Report the F statistic, both degrees of freedom, and the p-value in standard format — for example, F(1, 103) = 0.219, p = .641. Then state directly whether the assumption was or was not violated, and name the version of the t-test you will proceed with as a result. Two to three sentences is sufficient for this interpretation.
In JASP: open the grades.jasp file → go to T-Tests in the top menu → select Independent Samples T-Test → move Final to the Dependent Variables box and Review to the Grouping Variable box → under Assumption Checks, tick Equality of Variances (Levene’s) → the Levene’s test table will appear in your output panel. Copy the table and paste it into your DAA template using a screenshot or the copy function.
The table will show you the F statistic, df1, df2, and p-value. Those four numbers are what you report and interpret in Section 2.
Section 3: How to Approach Results and Interpretation
Section 3 is where you report what you actually found. It has several required components that need to appear in a specific order: the descriptive statistics, the t-test output table, a written report of the means and SDs, the t-test result in APA format, and the hypothesis decision.
The Format Is Standardized — Use It Exactly
APA style for t-test results follows a fixed structure. Deviating from it, even slightly, is a common grading point in quantitative courses. The elements are: the test statistic (t), degrees of freedom in parentheses, the value of t, and the p-value.
Standard format: t(df) = [value], p = [value]Reporting means and SDs: Use M = [value], SD = [value] — capital M for mean, capital SD for standard deviation, italicized in formal writing.
What “not significant” means: If p > .05, you do not reject the null hypothesis. You state that there is no statistically significant difference between the groups. Do not say the groups are “the same” — that is an overclaim. Say there is no statistically significant difference detected in this sample.
Section 4: How to Approach Statistical Conclusions
Section 4 asks you to step back and write a brief, critical evaluation of what the analysis found and what its limits are. This is not a restatement of Section 3. It is a synthesis plus a critique.
What the Summary Should Cover
- Restate the purpose of the analysis in one sentence
- Summarize the key finding: was there or was there not a significant difference, and what does that mean for the research question?
- State the hypothesis decision one final time — rejected or not rejected — and connect it explicitly to the p-value
- Keep it to three to five sentences; this is a summary, not a new discussion section
What the Limitations Should Address
- What the t-test cannot tell you — it only compares group means; it does not control for confounding variables
- Factors that might explain the results beyond what the test captured — prior academic preparation, study habits, motivation
- Generalizability — does this sample represent a broader population?
- Whether the intervention itself (the review session) was sufficiently different from regular instruction to produce detectable effects
Saying “the sample size may be a limitation” without explaining why, or listing limitations without connecting them to this specific study, scores lower than a focused discussion of two or three specific, relevant limitations. Think about what actually happened in this data set: two groups were compared on one outcome. What variables outside group membership could explain exam score differences? Name them. That is what a strong limitations paragraph looks like.
Section 5: How to Approach the Application Section
Section 5 asks you to move beyond the classroom data set and think about how the independent samples t-test applies in your actual professional field. For nursing and healthcare students, this is a real-world translation exercise — and it is marked on specificity, not generality.
Name a Real Independent Variable, a Real Dependent Variable, and Explain the Clinical or Research Significance
The section has three required elements: (1) a named independent variable that creates two distinct groups, (2) a named dependent variable that is continuous and measurable, and (3) an explanation of why studying this difference matters for your field or practice. Generic statements about “comparing groups in healthcare” do not satisfy the requirement. You need a specific scenario.
Structure for a strong response:Sentence 1: Describe the healthcare or nursing scenario and how the independent samples t-test applies to it.
Sentence 2: Name the independent variable and identify the two groups it defines. Example: type of intervention received (cognitive behavioral therapy vs. standard care).
Sentence 3: Name the dependent variable. Example: patient anxiety scores measured on a validated standardized instrument at 8 weeks post-intervention.
Sentence 4–5: Explain why this comparison matters. What clinical decision or practice improvement could the results inform? Tie it to evidence-based practice, patient outcomes, or quality of care.
Important: The independent variable must define exactly two groups. If your grouping variable creates three or more groups, the independent samples t-test is no longer appropriate and you would need ANOVA. Make sure your scenario uses a true two-group comparison.
Running the Full Analysis in JASP — What to Do in Order
Open grades.jasp and Verify the Data Set
Open JASP, load the grades.jasp file. Before running anything, confirm that the Review variable is set as nominal (categorical) and Final is set as continuous (scale). JASP uses small icons next to variable names in the data view. If Review shows as scale, right-click and change it to nominal. Getting the variable types correct in JASP ensures the software treats them correctly in the analysis.
Set Up the Independent Samples T-Test
Go to T-Tests → Independent Samples T-Test. Move Final to the Dependent Variables field. Move Review to the Grouping Variable field. JASP will ask you to identify the group values — enter 1 and 2 (or verify the grouping is correctly assigned). Under Tests, keep Student’s selected for now (you will decide whether to switch to Welch after viewing Levene’s results).
Enable Levene’s Test and Descriptives
Under Assumption Checks, tick Equality of Variances (Levene’s). Under Additional Statistics, tick Descriptives. Both tables will now appear in your output panel. Read the Levene’s result first. If p > .05, stay with Student’s. If p < .05, tick Welch under Tests and uncheck Student’s.
Copy Output Into the DAA Template
JASP allows you to right-click tables and copy them. Paste them as images into your Word document. Alternatively, screenshot the output panel. Make sure both the Descriptives table and the Independent Samples T-Test table are included in Section 3, and the Levene’s table is in Section 2. Each table should be legible — if the screenshot is blurry, use the copy function instead.
Mistakes That Cost Points in the DAA
Labeling Review as continuous
Review has numeric values (1 and 2), but those numbers are codes for group membership — not measurements. Calling it continuous is a fundamental classification error that undermines Section 1.
Identify Review as categorical with a brief justification
State that Review is categorical because it classifies students into groups based on attendance, and the numeric codes (1 and 2) are labels, not measured quantities. One sentence of reasoning is enough.
Skipping the Levene’s interpretation and going straight to the t-test
Pasting the Levene’s table without interpreting it — or not reading the p-value before deciding which t-test to run — means the assumption-testing step has no analytical content. It is just a table without meaning.
Report F, df, p, then state the assumption decision explicitly
Write out the values in APA format, state whether p is above or below .05, name whether the assumption is or is not violated, and name which t-test version you will use as a result. The grader needs all four of those elements.
Saying “the groups are the same” when the null is not rejected
A non-significant result does not mean the groups are identical. It means the data did not provide enough evidence to detect a statistically significant difference. These are different claims.
State there was no statistically significant difference detected in this sample
The precise language: “There was no statistically significant difference in [DV] between [group 1] and [group 2], t(df) = [value], p = [value].” Null not rejected. That is the correct framing.
Application section describes a three-group or continuous grouping variable
If your proposed healthcare example has more than two groups — say, three treatment types — the independent samples t-test no longer applies. That is an ANOVA scenario. The grader knows the difference.
Design your application example with exactly two groups
Treatment A vs. treatment B. Intervention vs. control. Screened vs. not screened. Two groups, one continuous outcome. That maps onto the t-test design and demonstrates you understand the test’s requirements.
Frequently Asked Questions
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The DAA is sequential. Section 1 sets up the logic. Section 2 checks the assumption that determines what you run. Section 3 runs the test, reports the output, and makes the hypothesis decision. Section 4 steps back and evaluates. Section 5 translates to professional practice. If any of those links breaks — a missed assumption interpretation, a wrong test version, a vague hypothesis decision — the sections after it start to look inconsistent.
Before you submit, run through this check: Does Section 3 use the t-test version indicated by Section 2? Does the hypothesis decision in Section 3 use the p-value from your JASP output? Does Section 4 specifically address limitations of the independent samples t-test, not just statistics in general? Does the application example in Section 5 describe exactly two groups and one continuous outcome? If yes to all four, the submission is structurally sound.