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01:960:401 · Statistics · School 01 — New Brunswick School of Arts and Sciences
Basic Statistics for Research is a research-oriented introductory statistics course that moves from describing and collecting data through probability, interval estimation, hypothesis testing, regression, contingency analysis, ANOVA, and nonparametric methods so students can choose and interpret methods for planned studies and observed data.
How hard?
Students rate teaching
SIRS · 5,950 responses · 2014–2025
Spring 2027
8 open in snapshot · 3 instructors
When it meets
C/D, Busch, Livingston, College Avenue, Online
Checked-in sections · current seat counts are unavailable.
AI-generated overview
Higher means more demanding · AI summary
5 units · topic sequence may vary by instructor
The archived Rutgers syllabus explicitly starts with describing and collecting data, exploring data, and statistical summaries. That emphasis matters: in this course, statistics is framed as part of a research workflow, so students are expected to think about study structure and data quality before jumping into significance tests.
The next block builds the uncertainty machinery behind inference. Official syllabus topics include introductory probability, discrete distributions, and normal and continuous distributions, which together explain what kinds of variability should be expected before any real sample is collected.
This is the core inference module. The syllabus calls out interval estimation, sample-size ideas, hypothesis testing, multiple-testing issues, and two-sample tests, so the course usually asks students not just to compute an answer but to decide what question is being tested and how uncertain the estimate remains afterward.
Research questions often move beyond one variable at a time, and the Rutgers syllabus reflects that. Regression and contingency-table work usually appear here as ways to describe association, compare fitted trends, and evaluate whether observed categorical patterns are stronger than random chance would predict.
The later course material broadens the toolkit for more realistic research settings. ANOVA handles multi-group comparisons under model assumptions, while nonparametric methods give students alternatives when the data or design make standard parametric procedures less persuasive.
Public r/rutgers discussion often treats 01:960:401 as one of the more approachable Rutgers statistics options, but the same threads also warn that workload and stress rise quickly in compressed terms or when students try to cram instead of keeping pace with the chapter-by-chapter rhythm.
A p-value or interval is only meaningful when the underlying study design makes sense. This course treats data collection and study structure as part of statistics, not as a separate prelude.
One-sample inference, two-sample tests, regression, contingency analysis, and ANOVA solve different problems. The point is to match the tool to the research claim instead of forcing every dataset through one favorite procedure.
Regression and ANOVA may look like separate units, but they reflect a shared idea: explain variation by connecting outcomes to predictors or group structure.
Nonparametric methods and assumption checks matter because real research data are messy. Good statistical practice means being honest about what the method can and cannot justify.
Common pitfall
Instead
Identify variables, groups, and sampling or experimental structure first; then decide whether the problem is estimation, comparison, association, or model fitting.
Common pitfall
Instead
A statistically detectable effect can still be too small, too noisy, or too context-dependent to matter for the actual research decision.
Common pitfall
Instead
Interpret coefficients, group effects, and assumptions in plain language so the model remains connected to the research question that motivated it.
8 open · C/D, Busch, Livingston, College Avenue, Online
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0116872 | Open | Luvalle | Monday 12:10 PM-1:30 PM at FSW 105; Thursday 12:10 PM-1:30 PM at FSW 105FSW 105 | C/D |
| 0216873 | Open | Dicrecchio | Tuesday 12:10 PM-1:30 PM at EN B120; Friday 12:10 PM-1:30 PM at EN B120EN B120 | Busch |
| 0316874 | Open | Dicrecchio | Monday 5:40 PM-7:00 PM at EN B120; Wednesday 5:40 PM-7:00 PM at EN B120EN B120 | Busch |
| 0416875 | Open | Wednesday 10:20 AM-11:40 AM at EN B120; Friday 3:50 PM-5:10 PM at EN B120EN B120 | Busch | |
| 0516876 | Open | Tuesday 7:30 PM-10:30 PM at TIL 232TIL 232 | Livingston | |
| 0616877 | Open | Thursday 5:40 PM-8:40 PM at MI 100MI 100 | College Avenue | |
| 0716878 | Open | Monday 8:30 AM-9:50 AM at MI 100; Thursday 8:30 AM-9:50 AM at MI 100MI 100 | College Avenue | |
| 9016879 | Open | Lawrence | ONLINE INSTRUCTION(INTERNET) | Online |
Historical student surveys
Teaching
3.63
Course quality
3.59
Response rate
41.4%
Coverage
128 offerings · 2014–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Michael Luvalle | 14 | 3.65 | 3.56 |
| Sheila Lawrence | 11 | 4.28 | 4.15 |
| Suryakala Srinivasan | 10 | 3.65 | 3.62 |
| Ravi Ramaswamy | 9 | 3.63 | 3.58 |
| Nicole Dicrecchio | 6 | 4.18 | 4.13 |
| Lawrence Sheila | 6 | 4.07 | 3.97 |
Catalog and planning context
Credits
3
Current campuses
C/D, Busch, Livingston, College Avenue, Online
Current availability
8 open of 8
Catalog terms
Fall, Spring, Summer, Winter
Core codes
ITR, QQ, QR
Loaded terms
8
Any Course EQUAL or GREATER Than: (01:640:112)
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