{"id":2826,"date":"2026-07-06T08:15:08","date_gmt":"2026-07-06T08:15:08","guid":{"rendered":"https:\/\/proexperta.com\/?p=2826"},"modified":"2026-08-12T16:55:30","modified_gmt":"2026-08-12T16:55:30","slug":"kako-odabrati-statisticki-test","status":"publish","type":"post","link":"https:\/\/proexperta.com\/en\/kako-odabrati-statisticki-test\/","title":{"rendered":"How to choose the right statistical test: a decision guide"},"content":{"rendered":"

Many students and researchers collect their data successfully, then stall at test selection. The survey is complete, the dataset exists and hypotheses are written, but clicking through SPSS menus will not reveal the right test: software does not know your research question.<\/p>\n\n\n\n

Choosing a statistical test follows a sequence of questions about your study. Answers usually narrow the options to an appropriate test and possible non-parametric alternative. This guide provides a decision sequence, reference table and dissertation examples. Comprehensive statistical analysis and interpretation<\/a>is also available as a separate service.<\/p>\n\n\n\n

First question: what do you want to test?<\/h2>\n\n\n\n

Choose a test from the research question, not the installed software. Most quantitative studies have one of a few basic aims.<\/p>\n\n\n\n

You may compare two groups, compare three or more groups, examine relationships between variables, predict an outcome from several factors, measure the same group before and after an intervention, or examine an association between categorical variables.<\/p>\n\n\n\n

Each aim leads to a different family of tests. Before opening software, complete the sentence: 'I want to test whether...'<\/p>\n\n\n\n

Identify your variable types first<\/h2>\n\n\n\n

Test selection also depends on variable types. Three broad levels provide a useful starting point.<\/p>\n\n\n\n

Nominal variables<\/strong> are unordered categories such as study programme or employment status. Numerical codes are labels, not quantities.<\/p>\n\n\n\n

Ordinal variables<\/strong> have an order but not necessarily equal intervals, such as education level or a single satisfaction item from very dissatisfied to very satisfied.<\/p>\n\n\n\n

Interval and ratio (numerical) variables<\/strong> have meaningful intervals, such as age, years of service, test scores or grade averages. Their means can be meaningful and they are commonly used in parametric analysis.<\/p>\n\n\n\n

A single Likert item is ordinal. A composite formed by summing or averaging several items is often treated as numerical in practice. This choice should be justified in the methodology rather than left implicit.<\/p>\n\n\n\n

Also distinguish variable roles: the dependent variable<\/strong> is the measured outcome; the independent variable<\/strong> is the factor whose relationship or group difference you examine. When comparing satisfaction between full-time and part-time students, satisfaction is the outcome and student status is the grouping variable.<\/p>\n\n\n\n

Finally, distinguish independent and paired samples<\/strong>. Independent samples contain different people in different groups; paired samples arise when the same people are measured repeatedly or observations are matched. This distinction directly affects test selection.<\/p>\n\n\n\n

Decision guide: choosing a statistical test<\/h2>\n\n\n\n

Once you know your aim and variable types, work through five questions.<\/p>\n\n\n\n

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  1. How many groups are you comparing?<\/strong> One against a reference value, two, or three or more. For association or prediction without group comparisons, move to question 5.<\/li>\n\n\n\n
  2. Are samples independent or paired?<\/strong> Different people in each group, or repeated measurements of the same people?<\/li>\n\n\n\n
  3. Is the outcome numerical or categorical?<\/strong> Numerical outcomes may lead to t-tests, ANOVA, correlation or linear regression. Categorical data may call for chi-squared tests or logistic regression.<\/li>\n\n\n\n
  4. Are the relevant normality assumptions reasonable?<\/strong> If so, and other assumptions hold, a parametric test may be appropriate. Otherwise, consider a suitable non-parametric alternative.<\/li>\n\n\n\n
  5. Are you comparing, examining association or predicting?<\/strong> Comparisons lead to difference tests, associations to correlations and prediction to regression.<\/li>\n<\/ol>\n\n\n\n
    Dijagram odlu\u010divanja za odabir statisti\u010dkog testa<\/title>Decision guide for selecting a statistical test by variable type, number of groups and sample design.<\/desc><\/marker><\/defs>How to choose a statistical test<\/text>What do you want to test?<\/text>Compare groups<\/text>Examine association<\/text>Predict an outcome<\/text>How many groups?<\/text>2 groups<\/text>3+ groups<\/text>independent t-test<\/text>\u00b7 Mann-Whitney U<\/text>paired t-test<\/text>\u00b7 Wilcoxon<\/text>ANOVA + post-hoc<\/text>\u00b7 Kruskal-Wallis<\/text>repeated-measures ANOVA<\/text>\u00b7 Friedman<\/text>Variable types?<\/text>numerical<\/text>categorical<\/text>Pearson correlation<\/text>\u00b7 Spearman<\/text>(ordinal \u2192<\/text>Spearman)<\/text>chi-squared test<\/text>Fisher (small<\/text>cell counts)<\/text>Outcome<\/text>type?<\/text>numerical \u2192<\/text>linear regression<\/text>categorical \u2192<\/text>logistic regression<\/text>Key selection principles<\/text>1. Research aim: comparison, association or prediction.<\/text>2. Group count and sample design: independent or paired.<\/text>3. Outcome type: numerical or categorical.<\/text>4. Assumptions: use a suitable parametric test when its assumptions hold; otherwise consider the non-parametric alternative shown after the dot.<\/text>starting question<\/text>aim \/ decision<\/text>suggested test \u00b7 non-parametric alternative<\/text>DV = dependent variable \u00b7 IV = independent variable \u00b7 Proexperta<\/text><\/svg>
    Test selection by research aim, group count, variable type and assumptions.<\/figcaption><\/figure>\n\n\n\n

    Quick statistical test guide<\/h3>\n\n\n\n

    The same sequence in a quick-reference table:<\/p>\n\n\n\n