Guides & Resources for Your Statistical Analysis
Free downloadable guides to help you choose the right tests, use SPSS with confidence, interpret your results correctly, and avoid the most common statistical mistakes.
Free downloadable guides to help you choose the right tests, use SPSS with confidence, interpret your results correctly, and avoid the most common statistical mistakes.
Each guide was written by our team of statisticians to address the questions students ask most often. Download them, bookmark them, and refer back whenever you need clarity.
Not sure whether your data calls for a t-test, ANOVA, chi-square, or Mann-Whitney? This guide walks you through the decision process step by step. You will learn the difference between parametric and nonparametric tests, understand when to treat variables as dependent or independent, and see clear examples of how each test applies to real research scenarios. By the end, you will be able to match any common hypothesis to the correct statistical method.
Download GuideSPSS can feel overwhelming the first time you open it. This guide takes you from zero to confident in a single read. Learn how to import data from Excel, check that your variables are set up correctly, run the most common statistical tests (descriptives, t-test, correlation, chi-square), and make sense of the output tables SPSS produces. Every step includes annotated screenshots so you always know exactly where to click and what each number means.
Download GuideRunning the analysis is only half the work. This guide focuses on what comes after: formulating correct conclusions from p-values and test statistics, understanding when a result is statistically significant versus practically meaningful, and avoiding the most common interpretation errors students make. You will also find a ready-to-use template for structuring the Results section of your thesis so that your findings are presented clearly and professionally.
Download GuideEven experienced researchers make avoidable errors. This guide covers the pitfalls we see most often: skipping or misapplying normality tests, drawing conclusions from small sample sizes without appropriate caution, confusing Pearson and Spearman correlations, and using linear regression when logistic regression is the correct choice. For each mistake, we explain why it matters and show you the right approach so your analysis holds up under scrutiny.
Download GuideOur free resources cover the fundamentals, but every dataset is different. Send us your project details and get a personalized quote for professional statistical analysis within 24 hours.