Practical experience supporting medical, operational, business, and questionnaire datasets with clear statistical analysis and interpretation.

Need professional statistical data analysis?
Professional statistical analysis for surveys, medical, operational, and business data using SPSS, Python, and Power BI.
Our numbers speak
Statistical analysis support for more than 180 survey, medical, operational, and professional data projects.
Support for 38 advanced data projects with organized tables, charts, and clear statistical interpretation.
Get an Assessment and Price for Your Data Analysis
Share a few details about your data and analysis requirements. We will review the request and recommend the right approach and price.
We provide data analysis, statistical consulting, and help interpreting outputs.
See What Your Results Can Look Like
A statistical report sample using fictional, anonymized data to demonstrate tables, charts, and clear output interpretation. Reports can be prepared in Arabic or English.
| Frequency | Percent | |
|---|---|---|
| Immediately after waking up | 19 | 13.4 |
| Within the first hour | 18 | 12.7 |
| After several hours | 53 | 37.3 |
| Usually do not watch in the morning | 52 | 36.6 |
| Total | 142 | 100.0 |
As shown in Table 3, most participants either started watching after several hours (37.3%) or usually did not watch in the morning (36.6%).
| Frequency | Percent | |
|---|---|---|
| Do not watch before bedtime | 36 | 25.4 |
| 30-60 minutes | 54 | 38.0 |
| 2-3 hours | 42 | 29.6 |
| 3-5 hours | 10 | 7.0 |
| Total | 142 | 100.0 |
The most common duration before bedtime was 30-60 minutes (38.0%), followed by 2-3 hours (29.6%).
4.2 Level and Patterns of Short-Form Video Use
Analysis objective: Determine the level of short-form video use.- TikTok 63%
- Instagram 13%
- YouTube 1%
- Facebook 23%
TikTok was the most commonly used platform (63.0%), followed by Facebook (23.0%) and Instagram (13.0%), while YouTube was the least used (1.0%).
| Frequency | Percent | |
|---|---|---|
| Suggested by platform algorithm | 11 | 7.7% |
| Shared by friends | 32 | 22.5% |
| Searching for them | 33 | 23.2% |
| Random scrolling | 66 | 46.5% |
| Total | 142 | 100.0% |
Pricing
Simple, transparent packages based on how much support your project needs. The final quote is confirmed after reviewing your request and before any work starts.
Best for small surveys or a single statistical test.
- Data coding and cleaning
- Descriptive statistics
- Multiple tables and charts
- Short result summary
Best for medium-sized data analysis and questionnaire projects.
- Everything in Basic
- Significance testing (T-test, ANOVA, Chi-square)
- Correlation analysis
- Detailed interpretation
- Multiple tables and charts
Best for advanced statistical analysis and complex or medical datasets.
- Everything in Standard
- Regression analysis
- Full reliability analysis (Cronbachs alpha)
- Assumption checks and reporting
- Support for statistical output review
Final pricing depends on data size, number of variables, and the tests required. You will always know the exact cost before work begins, and payment is made after you review the results.
How it works
Send your topic, data file, questionnaire, and required tests.
We confirm the analysis approach, timeline, and deliverables.
Get statistical outputs, tables, charts, and interpretation ready for review.
Payment is made after you receive the work, review it, and confirm the required outputs.
Services
We help analyze data and turn it into clear statistical outputs. Services include data cleaning, descriptive and inferential analysis, significance testing, correlation, regression, tables, charts, and output interpretation using SPSS, Python, and data analysis tools.
Coding, cleaning, descriptive tables, and survey result summaries.
Frequencies, means, standard deviations, charts, and cross-tabulations.
T-test, ANOVA, chi-square, non-parametric tests, and clear decisions.
Pearson/Spearman correlation, linear regression, and model interpretation.
Cronbach's alpha, scale checking, and item-level review.
Clear statistical explanation of outputs, tables, charts, and key findings.
Frequently Asked Questions About Statistical Analysis
What is the difference between descriptive and inferential analysis?
Descriptive analysis summarizes data using frequencies, means, and percentages, while inferential analysis uses tests such as T-test and ANOVA to evaluate statistical significance, differences, and relationships.
Can you analyze questionnaire data that has not been coded yet?
Yes. The service includes reviewing the questionnaire items, coding the responses, and identifying the measurement level for each variable before starting the statistical analysis, even if the data is in a raw, unorganized file.
What types of data analysis do you offer?
We offer descriptive and inferential analysis, correlation and regression, reliability and validity analysis, group comparisons, and non-parametric analysis, depending on your data type and analysis objectives.
Do you need my own copy of SPSS to analyze my data?
No. We analyze your data using our own tools (SPSS, jamovi, Python, or Power BI as needed), and provide organized outputs together with a clear statistical explanation.
How do I know which statistical test fits my data?
The right test depends on the type of variables, measurement level, number of groups, and analysis objectives. We help you identify the suitable test after reviewing your data and goals.
Does the service include a simplified explanation of the results?
Yes. You receive a clear statistical explanation for each result, including its practical meaning and relationship to the stated analysis objectives.
What people say
A selection of client feedback after receiving statistical analysis and result interpretation services.
Articles to Support Your Data Analysis
Practical guides to analysis decisions and clear interpretation of results.

How to Choose the Right Statistical Test
A practical guide to choosing a statistical test based on the analysis objective, variable types, groups, and data assumptions.
Read article
Preparing Questionnaire Data for SPSS Analysis
A clear workflow for coding, cleaning, and checking questionnaire data before statistical analysis in SPSS.
Read article
How to Interpret a p-value Correctly
A concise explanation of p-values, statistical significance, effect size, and confidence intervals for statistical reporting.
Read article
Data Analysis Support Workflow
See how data analysis support progresses from reviewing datasets and questionnaires to choosing tests, interpreting results, and preparing clear outputs.
Read articleTools used
We use professional statistical and data visualization tools according to the project needs and required outputs.
Ready to analyze your data?
Send your requirements on WhatsApp and receive a clear response about the suitable analysis, timeline, and next steps.