Acadata Insight Consult
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Smart statistical tools for student research

Your all-in-one free statistical toolkit. Find the right test, understand p-values, check assumptions, run regression, calculate confidence intervals, determine sample size and power, clean survey data, assess reliability (Cronbach’s alpha), and choose the best charts. Designed for undergraduate, MSc, MPhil and PhD students.

Test Finder P-value Charts Confidence Interval Assumptions Regression Descriptive Stats Cronbach’s Alpha Survey Cleaning Power Sample Size

Statistical Test Recommender

Select the options that best match your study design.

Your recommended statistical test

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Note: this tool gives a general recommendation. Final test selection may still depend on assumptions, sample size, study design, and variable coding.

Need help running the test, interpreting the output, or writing your results? Acadata Insight Consult can help you clean your data, run the right analysis, create publication-ready tables and figures, and prepare for defense questions.

P-value Explainer

Enter your p-value and get a plain-language explanation you can understand.

P-value explanation

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Want help interpreting your SPSS, R, Stata, or Excel output correctly? We can help you explain significance, confidence intervals, effect sizes, tables, charts, and result write-up for your thesis.

Chart Chooser

Answer a few quick questions to find the most suitable chart for your data.

Recommended chart

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Need a clean, thesis-ready chart made for your results section? We can design publication-style charts, tables, and figure legends that are clear, professional, and easy to defend before supervisors.

Confidence Interval Calculator

Estimate a confidence interval around a sample mean using your summary statistics.

Confidence interval result

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Need help reporting confidence intervals the right way? We can help you calculate, interpret, and write confidence intervals for means, proportions, odds ratios, regression coefficients, and model outputs.

Assumption Checker

Check whether a parametric analysis is likely appropriate, or whether you should consider a non-parametric alternative.

Assumption check summary

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Regression Model Recommender

Choose a more suitable regression model based on your outcome type and data structure.

Recommended regression model

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Need help fitting the right model and interpreting the coefficients?We can help with linear, logistic, multinomial, ordinal, Poisson, negative binomial, mixed models, survival models, diagnostics, and publication-ready reporting.

Descriptive Statistics Interpreter

Enter basic summary statistics and get a plain-language interpretation for your dataset.

Descriptive interpretation

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Cronbach’s Alpha / Reliability Guide

Interpret the internal consistency of your questionnaire, scale, or survey instrument.

Reliability interpretation

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Below 0.60Usually suggests poor internal consistency.
0.70 to 0.79Often considered acceptable in many research settings.
0.80 and aboveGenerally suggests good to very good reliability.

Survey Data Cleaning Checklist

Generate a simple checklist of what to inspect before analysis, reporting, or dashboarding.

Your survey cleaning checklist

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Need full survey cleaning, coding, reliability testing, and analysis?We help clean messy survey files, recode variables, score scales, test reliability, build dashboards, run inferential analysis, and prepare final reports.

Power Calculator

Estimate approximate statistical power for a two-group comparison using effect size, sample sizes, and significance level.

Estimated statistical power

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Need help planning a study with enough power?We can help determine defensible sample sizes, effect size assumptions, analysis plans, and protocol-ready statistical justifications.

Sample Size Estimator

Estimate sample size for a proportion using confidence level, margin of error, and expected prevalence.

Estimated sample size

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Need a defendable sample size for your thesis or grant?We can help refine prevalence assumptions, design effects, non-response adjustments, and write the full sample size justification for your methodology.

Tips for students and researchers

  • A p-value does not tell you how large or important an effect is.
  • The best chart depends on your message, not just the dataset.
  • The right statistical test still depends on assumptions and study design.
  • Always combine statistical significance with subject-matter interpretation.
  • Reliability, cleaning quality, and variable coding strongly affect the final results.