Descriptive and Inferential Statistics Analysis for Researchers and Organisations

What our descriptive and inferential statistics service covers

• Frequencies, percentages, means, medians, standard deviations and ranges for every variable
• Distribution checks: skewness, kurtosis, normality tests and outlier screening
• Cross-tabulations and group comparisons with the right test for each pair of variables
• Confidence intervals and margins of error for means, proportions and differences
• Tests of differences and associations, with effect sizes next to every p-value
• Weighted estimates for surveys that use sampling weights or clustering
• Report-ready tables and charts in APA or your organisation’s house style

Descriptive statistics: summarise before you test

Descriptive statistics tell the reader what is in your data before any test is run. We report the centre of each variable (mean or median), its spread (standard deviation or interquartile range) and its shape, and we flag outliers and skewed variables that would make a simple average misleading. For categorical data we give counts and percentages with the denominator stated. You can check a quick calculation yourself with our free standard deviation calculator.

Inferential statistics: from your sample to the population

Inferential statistics answer whether what you see in a sample is likely to hold in the wider population. We choose the confidence intervals, tests and effect sizes that match your design, sample size and measurement scale, and we report them completely: estimate, interval, test statistic, p-value and effect size. A p-value alone never shows how large or how important a difference is, so we always add an effect size and a plain-language reading. If you are unsure which test applies, try the test selector, and use the p-value calculator to turn a test statistic into an exact p-value.

Once the basics are in place, the next step is usually hypothesis testing to compare groups or regression analysis to model relationships. If the file needs tidying first, see our data cleaning and preparation service.

How the work runs

1. You share the dataset, questionnaire or codebook and your research questions.
2. We review the data, agree an analysis plan and flag problems such as missing values or very small groups.
3. We run the analysis and check the assumptions behind each method.
4. You receive tables, charts and a written interpretation, and we answer follow-up questions.

What you receive

• Output files plus syntax or scripts, so the analysis can be repeated
• Tables formatted for your report, thesis or journal article
• A short methods paragraph describing exactly what was done
• A plain-language interpretation of each result