What this site is for
Most statistics calculators online give you an answer and nothing else. You paste in numbers, a figure appears, and you are exactly as capable as you were before. That is fine if you already know what you are doing and just want to avoid arithmetic. It is useless if you are a student trying to understand a method, a researcher checking a result by hand, or an analyst who needs to explain to someone else how a figure was reached.
DatrixCalc is built the other way round. Every instrument here shows the intermediate steps: the deviations from the mean, the sum of squares, the critical value it looked up and why, the degrees of freedom it used. The final number is the smallest part of the output.
Everything runs in your browser. Nothing you paste is transmitted, logged or stored — there is no server-side processing on this site at all. That matters more than it sounds for statistics work, because the data people paste into these tools is frequently confidential: unpublished research, clinical measurements, commercial figures, exam marks.
Start with the right instrument
The five tools currently online cover the sequence most analyses actually follow, and they chain together. A short guide to which one you want:
| If your question is… | Use |
|---|---|
| What does this data look like overall? | Mean, Median & Mode |
| How spread out is it? | Standard Deviation |
| How unusual is this one value? | Z-Score |
| How precise is my estimate of the average? | Confidence Interval |
| Do these two variables move together? | Linear Regression |
The three ideas everything else is built on
Centre
A single number standing in for a whole dataset. The mean is the arithmetic average and uses every value, which makes it powerful and fragile in equal measure — one extreme observation drags it. The median is the middle value once sorted, and ignores how extreme the extremes are, which makes it robust. The mode is the most frequent value, and is the only one of the three that works on categorical data.
The choice between them is not a technicality. Reporting mean household income where a handful of very high earners exist produces a figure almost nobody actually earns; the median describes the typical household far better. Any time mean and median diverge substantially, the distribution is skewed, and that divergence is itself a finding worth reporting.
Spread
Two datasets can share a mean and be nothing alike. Standard deviation measures the typical distance of an observation from the mean, in the original units. It is the foundation of almost everything inferential: z-scores are distances measured in standard deviations, confidence intervals are built from the standard error which is derived from the standard deviation, and effect sizes express differences in standard deviation units.
Uncertainty
Any statistic computed from a sample is an estimate, and estimates have error bars. A confidence interval expresses that directly: a range of plausible values for the true population figure, given what you observed. It is the single most useful habit in applied statistics, because it forces the honest question that a bare point estimate lets you dodge — how much would this number move if I collected a different sample?
Common mistakes these tools will not catch
A calculator validates arithmetic, not reasoning. The most consequential errors in statistics happen before any number is entered:
- Correlation read as causation. Ice cream sales correlate with drownings. Both track summer temperature. A regression line has no opinion on which variable causes which, or whether a third variable drives both.
- The wrong measure of centre. Using a mean on strongly skewed data — income, response times, house prices — produces a technically correct number that misdescribes the typical case.
- Population formula on a sample. Understates spread, which narrows confidence intervals and inflates apparent significance. The error compounds through every downstream calculation.
- Extrapolating a regression. A line fitted between x = 10 and x = 50 says nothing reliable about x = 200. The relationship may be linear only within the range you observed.
- Ignoring how the data arrived. No amount of correct arithmetic rescues a biased sample. If your respondents self-selected, your confidence interval is a precise statement about a group that does not represent the population you care about.
Notes on precision and method
A few things worth knowing about how these instruments compute:
- Quantiles use linear interpolation between order statistics — the method R calls type 7 and the one Excel's PERCENTILE.INC uses. Other conventions exist and will give slightly different quartiles on small datasets. Where it matters, the tool says which it used.
- The normal cumulative distribution is evaluated with the Abramowitz & Stegun 7.1.26 rational approximation, accurate to about 1.5 × 10−7 — far tighter than any published table.
- Results are displayed to four decimal places by default, switching to scientific notation for very large or very small magnitudes. Full precision is retained internally throughout; only the display is rounded.
- Every tool states its assumptions on the page. Where a method requires approximate normality, a minimum sample size, or independent observations, that is written down rather than left implicit.
What is queued
Twenty-seven further instruments are planned, in rough priority order: p-value from any test statistic, A/B test significance, one- and two-sample t-tests, combinations and permutations, the normal, binomial and Poisson distributions, percentiles and quartiles, outlier detection, chi-square, odds ratio and relative risk, and a frequency distribution builder. We add tools slowly and finish them properly rather than shipping thin pages. If there is one you keep needing, send it through — requests reorder the queue.