Statistical Analysis for Research Data
ResearchTry's statistical analysis workspace helps you move from a raw spreadsheet to structured, publication-ready results. You upload your master chart or dataset as an Excel or CSV file, review how each variable has been read, and choose the analyses that match your study objectives. The platform then produces descriptive tables, inferential test results, graphs and plain-language interpretation paragraphs that you can review, edit and export.
The goal is not to replace statistical judgement but to remove the repetitive work that sits between collecting data and writing a results chapter: recoding categories, building frequency tables, running the same comparison across many variables, and rewriting every p-value into a sentence. Calculations keep full internal precision, while displayed values are rounded consistently to two decimal places and p-values are reported meaningfully (for example, p < 0.001 rather than 0.00).
Who it is designed for
- Postgraduate medical, dental, nursing and allied health students preparing a thesis or dissertation
- Faculty and guides who review student results chapters
- Clinical researchers analysing hospital or community study data
- Public health and social science researchers working with survey data
Key capabilities and benefits
Data import and variable review
Upload Excel or CSV files. Variable types (numeric, categorical, ordinal) are detected and can be corrected before any analysis runs, and inconsistent category labels can be normalised.
Descriptive statistics
Frequencies and percentages for categorical variables, and mean, standard deviation, median and interquartile range for continuous variables, arranged in thesis-style tables.
Group comparisons
Chi-square and Fisher's exact tests, independent and paired t-tests, Mann-Whitney U, ANOVA and Kruskal-Wallis, with the test chosen according to your variable types and groups.
Correlation and regression
Correlation coefficients and linear regression output reported with coefficients, confidence intervals and model fit where applicable.
Graphs
Bar charts, pie charts, box plots and other graphs generated from the same data as your tables, with editable titles and labels.
Interpretation and export
Each table can be accompanied by an interpretation paragraph. Results export to Word (DOCX), PDF and Excel for use in your thesis or manuscript.
How it works
- Step 1
Upload your data
Add your master chart as an Excel or CSV file and confirm how each column should be treated.
- Step 2
Select analyses
Choose descriptive summaries, comparisons or associations that answer your objectives, or let the platform suggest suitable tests for your variable types.
- Step 3
Review and export
Check tables, graphs and interpretations, edit wording where needed, and download them in Word, PDF or Excel format.
Research use cases
- Comparing outcomes between a case group and a control group in a hospital-based study
- Summarising baseline characteristics of participants in a cross-sectional survey
- Testing the association between risk factors and a disease outcome
- Preparing the results chapter of an MD, MS, MDS or MSc thesis
- Producing tables for a journal manuscript or conference abstract
Methodology and accuracy guidance
Statistical tests are only as valid as the data and the study design behind them. Check that your data are clean, that missing values are handled appropriately, and that each variable is coded correctly before interpreting any output.
Parametric tests such as the t-test and ANOVA assume approximately normal distributions and similar variances. Where these assumptions are doubtful, non-parametric alternatives are available and should be considered.
A statistically significant p-value does not by itself indicate clinical importance. Report effect sizes and confidence intervals alongside p-values, and interpret results in the context of your study objectives.
Automatically generated interpretation paragraphs are a starting draft. Review them with your guide or a statistician before final submission.
Frequently asked questions
What file formats can I upload?
You can upload Excel (.xlsx, .xls) and CSV files. Each column should represent one variable and each row one participant or observation.
Do I need to know which statistical test to use?
It helps, but it is not required. The platform suggests suitable tests based on your variable types and the number of groups, and you can change the selection before running the analysis.
How are p-values displayed?
Values are shown with up to two decimal places. Small p-values are shown with three decimals, values below 0.001 are reported as p < 0.001, and a nonzero p-value is never shown as 0.00.
Can I edit the tables and interpretation text?
Yes. Tables, graph labels and interpretation paragraphs can be reviewed and edited before you export them.
Is my uploaded data private?
Your data is stored in your own account and is accessible only to you after sign-in. Please read our privacy policy for full details.
Related ResearchTry tools
- Sample Size CalculatorCalculate sample size for observational, experimental, diagnostic, correlation and clinical research designs with guided inputs.
- Synopsis PlannerOrganize objectives, methodology, variables, timelines and analysis plans with a guided research synopsis workflow.
- Thesis FormattingFormat thesis chapters, references, tables, figures and front matter according to academic and institutional guidelines.
Ready to begin?
Open the Statistical Analysis tool and start your work.