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Free Chrome extension Statistics

Statistics Problem Solver: from p-values to plain English

SolveTap's Statistics Problem Solver is a free browser extension that reads a screenshot of a statistics problem — including data tables and software output — and walks through the solution step by step: which test applies and why, whether the conditions hold, the calculation itself, and what the result actually means in context.

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Why statistics trips students up

Statistics is less about computing and more about deciding: one-tailed or two-tailed? z or t? Is this a proportion or a mean? Most lost points come from choosing the wrong procedure, mixing up the tails, or ending with a number and no interpretation — not from arithmetic.

So the Statistics Problem Solver mirrors the routine your course grades you on. It identifies the parameter, checks the conditions, states hypotheses before computing anything, and always closes with the sentence in context — "at the 5% level, there is not enough evidence that…" — because that sentence is usually where the marks are.

It also catches the classic traps as it works: reading the wrong tail of the distribution, treating a paired design as independent samples, confusing statistical significance with practical importance, and rounding intermediate values so aggressively that the final answer drifts.

What it covers

  • Descriptive statistics Mean, median, spread, boxplots, outliers
  • Probability Rules, conditional probability, independence
  • Distributions Binomial, geometric, normal, uniform
  • Sampling distributions Central Limit Theorem, standard error
  • Confidence intervals Means and proportions, margin of error
  • Hypothesis tests z, t, paired, two-sample, proportions
  • Chi-square tests Goodness of fit, independence
  • Correlation & regression Slope, intercept, r, r², residuals
  • Interpretation Plain-English conclusions in context

Conditions, calculation, conclusion

Worked examples

These are the kinds of explanations the Statistics Problem Solver produces from a single screenshot.

One-sample t-test for a mean

Hypothesis testing

A cereal box claims a mean fill of 50 g. A random sample of 25 boxes has mean 51.2 g and standard deviation 4.0 g. At α = 0.05, is there evidence the true mean differs from 50 g?

  1. Hypotheses. H₀: μ = 50 vs. Hₐ: μ ≠ 50 (two-tailed — "differs," not "is more").
  2. Choose the test and check conditions. Population σ is unknown and n = 25, so use a one-sample t-test (random sample; box weights roughly normal or n moderately large).
  3. Compute the statistic. t = (x̄ − μ₀)/(s/√n) = (51.2 − 50)/(4.0/√25) = 1.2/0.8 = 1.50, with df = 24.
  4. Compare. The two-tailed critical value at α = 0.05 with df = 24 is ±2.064. Since |1.50| < 2.064 (p ≈ 0.147), the statistic is not in the rejection region.

Fail to reject H₀: at the 5% level, this sample does not give convincing evidence that the true mean fill differs from 50 g.

Common mistake the solver flags: saying "we accept H₀" or "the mean is 50 g." Failing to reject is not proof the claim is true — only that this sample lacks evidence against it.

Normal distribution probability

Distributions

Adult heights in a population are approximately normal with mean 170 cm and standard deviation 8 cm. What proportion of adults are taller than 182 cm?

  1. Standardize. z = (x − μ)/σ = (182 − 170)/8 = 1.50.
  2. Read the correct tail. The question asks for "taller than," so we need the area to the right of z = 1.50. Tables give the left area: P(Z < 1.50) = 0.9332.
  3. Complement. P(Z > 1.50) = 1 − 0.9332 = 0.0668.

About 6.7% of adults are taller than 182 cm.

Common mistake the solver flags: reporting 0.9332. That's the proportion shorter than 182 cm — a wrong-tail answer, and the single most common error on this problem type.

Interpreting a regression line

Regression

For a class dataset, the least-squares line predicting exam score from hours studied is ŷ = 12.5 + 3.2x, with r² = 0.64. Interpret the slope and r², and predict the score for 10 hours of study.

  1. Slope in context. For each additional hour studied, the predicted exam score increases by 3.2 points, on average.
  2. r² in context. 64% of the variation in exam scores is explained by the linear relationship with hours studied; the remaining 36% is due to other factors.
  3. Predict. ŷ = 12.5 + 3.2 × 10 = 44.5.
  4. Mind the caveats. Prediction is only reliable within the range of the data, and association is not causation — studying and scores may both relate to other variables.

Predicted score for 10 hours ≈ 44.5 points; slope = +3.2 points per hour; the model explains 64% of score variation.

Common mistake the solver flags: interpreting the intercept (12.5) as meaningful. It is the predicted score at zero hours — an extrapolation that may sit outside the observed data.

How it works for statistics

  1. Capture the problem. Screenshot the question with its data table or software output — summary statistics in the table are part of the problem.
  2. Confirm the setup. The solver names the parameter, the procedure and the conditions it checked, so you catch a wrong-test choice before any math happens.
  3. Follow through to the conclusion. Formula in symbols, substitution with values, decision rule, and a plain-English conclusion in context — the full chain your rubric expects.

Ask follow-ups like "why a t-test and not z?" or "what would change if this were one-tailed?" — comparing setups is how the decision-making finally clicks. More on our learning-first approach is on the academic integrity page.

Choosing the right procedure: three questions

Most "which test do I use?" confusion collapses into three questions the solver asks on every problem — and that you can ask yourself on a test:

  • What type of data? Means and measurements point to z- or t-procedures; counts in categories point to proportions or chi-square; two quantitative variables point to correlation and regression.
  • How many groups, and are they linked? One sample against a claimed value, two independent groups, or paired measurements on the same subjects each get a different procedure — treating paired data as independent is a classic silent error.
  • Do you know σ? Almost never in real problems. Unknown population standard deviation with sample data means t, not z — and the degrees of freedom follow from the sample size.

Answer those three and the formula chooses itself. The Statistics Problem Solver writes this decision out explicitly at the top of each solution, which is exactly the justification most rubrics award points for.

Statistics questions

Statistics Problem Solver FAQ

Which statistics topics does the solver cover?

Descriptive statistics, probability rules, discrete and continuous distributions, sampling distributions, confidence intervals, hypothesis testing (z, t, chi-square, proportions), correlation and linear regression — the span of a typical AP Statistics or intro college course.

Does it explain which test to use and why?

Yes. Choosing the wrong test is the most common statistics mistake, so every solution starts by identifying the parameter of interest, checking the conditions, and naming the test before any calculation.

Can it read data tables and software output?

Yes. Screenshot the table or output block along with the question. The solver reads values from tables and typical regression or test output and uses them in the worked solution.

Does it interpret results in plain English?

Yes. Every numeric result ends with an interpretation sentence in context — what the p-value, interval or slope actually says about the situation — because that sentence is usually what earns the marks.

Is the Statistics Problem Solver free?

Yes. It installs free from the Chrome Web Store and works without an account or payment card.

Try it on tonight's stats homework

Free, no sign-up, and every conclusion in plain English. Your data is never sold — see the privacy policy.

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