📚 Statistics Basics
Statistics doesn’t have to be complex — you can learn every key concept in the clearest possible way. This section explains the statistical foundations you’ll need when using MerQur, with real-life examples.
Without drowning in formulas, we explain what concepts mean and what they’re used for.
🎯 Which Topic Do You Want to Learn?
Statistics is the art of turning confusing piles of numbers into meaningful information — a way of telling a story…
📊Not all numbers are equal! Knowing your data type is the first step to choosing the right analysis. Explore 4 different data types…
🎯Want to describe a group with a single number? You have three “center” options: mean, median, mode. Each tells a different story…
📏Two groups may share the same mean and still be very different. Dispersion measures tell you how “spread out” the group is…
🔔Nature’s favorite shape! Height, IQ, blood pressure, measurement errors… all fit this bell-shaped curve. The Normal distribution is the cornerstone of statistics…
🧐Statistics’ most powerful weapon! Mathematically answers: “Is this a real difference, or just chance?” Used by scientists…
🎲The most widely-misunderstood concept in science! What the p-value does and doesn’t tell you — explained simply.
💪The p-value tells you if there is a difference. Effect size tells you how big it is. Modern statistics requires both…
🎯Far more informative than a single estimate! A confidence interval tells you where the true value most likely lies.
🔗Do two variables change together? Correlation measures it. But beware: changing together doesn’t mean one causes the other…
📈How much does one variable affect another? Can we predict a new observation? Regression takes correlation one step further…
⚖Hypothesis testing isn’t perfect — there’s always a risk of error. Two error types and the trade-off between them is fundamental.
👥How many participants are enough? Too few → underpowered study. Too many → wasted resources. The right number is computed mathematically.
🧪Which test? Parametric or non-parametric? Making the right choice based on your data structure is the key to a successful analysis.
📈When you want to compare three or more groups, a t-test isn’t enough. ANOVA takes the stage — comparing multiple means in one step.
🌍Measuring the entire world is impossible. Inferring about a large group (population) from a small subset (sample) — that’s the heart of inferential statistics.
🎲The mathematical foundation of statistics! Probability = the chance of an event happening. This page introduces probability with real-life examples.
📖 Suggested Reading Order
- What is Statistics? — basic logic
- Data Types — the difference between numbers
- Mean, Median, Mode — summary numbers
- Measures of Dispersion — variability
- Normal Distribution — the bell curve
- Probability Basics — mathematical foundation
- Sample and Population — who provides the data?
- Hypothesis Testing — real or chance?
- p-Value — significance
- Type I / Type II Error — guarding against mistakes
- Effect Size — how big?
- Confidence Interval — uncertainty range
- Sample Size — how many participants?
- Parametric vs Non-parametric — which test?
- Correlation — measuring association
- Regression — prediction models
- ANOVA — comparing multiple groups
- Field, A. (2018). Discovering statistics using IBM SPSS Statistics (5th ed.). Sage.
- Tukey, J. W. (1977). Exploratory data analysis. Addison-Wesley.
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
- American Psychological Association. (2020). Publication manual of the American Psychological Association (7th ed.).
- Wasserstein, R. L., & Lazar, N. A. (2016). The ASA’s statement on p-values. The American Statistician, 70(2), 129–133.