TL;DR — Quick Answer Correlation means two variables are related — they tend to change together. Causation means one variable actually causes a change in the other. The crucial principle is that correlation does not imply causation: just because two things are related does not mean one causes the other. A correlation can arise from […]
Read More →TL;DR — Quick Answer Statistical significance indicates whether a result is likely to reflect a real effect rather than random chance. It is assessed using the p-value — the probability of obtaining the observed result (or a more extreme one) if there were truly no effect. A common threshold is p < 0.05, meaning less […]
Read More →TL;DR — Quick Answer Data analysis is the process of examining, cleaning, organising, and interpreting data to extract meaningful insights and answer research questions. Quantitative data analysis uses statistical methods — descriptive statistics (summarising data) and inferential statistics (drawing conclusions and testing hypotheses). Qualitative data analysis uses interpretive methods like thematic analysis to identify patterns […]
Read More →TL;DR — Quick Answer The best AI tools for data analysis in 2026 include SPSS and its AI assistant for statistics, Julius AI and ChatGPT’s data analysis mode for conversational analysis, R with AI coding assistants for advanced work, and Python with AI support for large-scale analysis. For researchers without coding skills, Julius AI and […]
Read More →TL;DR — Quick Answer Sample size is the number of participants or data points needed in a research study to produce statistically reliable results. For most survey-based research, a minimum of 30 participants is required for basic statistical tests, 100 to 200 for reliable quantitative studies, and 300 to 400 for structural equation modelling. Sample […]
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