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What statistics essays cover
Our statistics writers work across the whole discipline and the methods and evidence UK markers expect you to engage with:
| Descriptive statistics | summarising and visualising data. |
| Probability | distributions, random variables and the foundations of inference. |
| Hypothesis testing | t-tests, ANOVA, chi-square and p-values. |
| Regression and modelling | linear, logistic and multivariate models. |
| Experimental design | sampling, power and study design. |
| Statistical software | SPSS, R, Python and Stata analysis. |
| Bayesian and non-parametric methods | alternative approaches for real data. |
How to write a first-class statistics essay
Strong statistics marks come from choosing the right method and interpreting results correctly, not just description. This is the structure UK markers reward:
- Introduction — define the key terms and state your line of argument.
- Set out the evidence — explain the relevant methods, tests and evidence accurately and concisely.
- Apply and analyse — link directly to the exact question and to real examples.
- Evaluate critically — check the assumptions, validity and interpretation of results. This is where most marks are won.
- Conclusion — reach a justified judgement that answers the question.
- References — a complete APA or Harvard reference list, with every claim cited.
Send us your title and marking criteria and we will produce a model essay that shows exactly this.
Frequently Asked Questions
Prices start from 6.99 per page. The final price depends on academic level, word count, and deadline.
A writer with a verified degree in Statistics or related field. All UK-based with at least a Masters degree.
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Harvard, APA, MLA, Chicago, OSCOLA, Vancouver, and any other style your university requires.
Explain the relevant methods, tests and evidence, apply them to the exact question, and critically evaluate the assumptions, validity and interpretation of results rather than just describing. Our writers can produce a model essay that shows exactly this.
Most UK statistics courses use APA or Harvard. We follow your department’s exact style, with every source cited and a complete reference list.
All of them, including descriptive statistics, probability, hypothesis testing, regression and modelling, experimental design, statistical software, bayesian and non-parametric methods.
What UK Students Say About Statistics Essays
★★★★★
"My probability theory essay required applying measure-theoretic foundations — Kolmogorov 1933 axioms — probability as measure on measurable space — sigma-algebra; probability measure axioms: non-negativity P(A) >= 0; normalisation P(Omega) = 1; countable additivity; conditional probability P(A|B) = P(AnB)/P(B); Bayes Theorem P(A|B) = P(B|A)P(A)/P(B); random variables — discrete vs continuous — pmf; pdf; cdf; expectation; variance; moment generating functions; Central Limit Theorem. EasyMarks delivered the probability theory precision my Warwick Statistics essay required."
— BSc Statistics, University of Warwick
★★★★★
"Writing about statistical inference required applying hypothesis testing rigorously — Neyman-Pearson framework — null and alternative hypotheses — test statistic — rejection region — Type I error alpha = P(reject H0 | H0 true); Type II error beta = P(fail to reject H0 | H1 true) — power = 1-beta; p-value interpretation — Ioannidis and the replication crisis; multiple testing — Bonferroni correction; false discovery rate — Benjamini-Hochberg; p-hacking — Simmons et al 2011; pre-registration. EasyMarks understood the statistical inference depth my Edinburgh essay demanded."
— MSc Statistics, University of Edinburgh
★★★★★
"My regression essay required applying OLS rigorously — Gauss-Markov theorem — BLUE estimator — assumptions: linearity; independence; homoscedasticity; normality; R-squared; multiple regression — multicollinearity — VIF; residual diagnostics; GLMs — logistic regression for binary outcomes — log-odds — odds ratio interpretation; Poisson regression for count data; mixed effects models — random intercepts; random slopes; LASSO and Ridge regularisation — bias-variance tradeoff; cross-validation. EasyMarks delivered the modelling depth my LSE Statistics essay required."
— MSc Statistics, London School of Economics
Common Statistics Essay Challenges
Probability Theory Foundations
Probability theory requires rigorous measure-theoretic foundations — Kolmogorov 1933 Grundbegriffe der Wahrscheinlichkeitsrechnung — probability as measure on measurable space (Omega, F, P) — sigma-algebra F closed under complement and countable union — Borel sigma-algebra on R generated by open intervals; probability measure axioms: non-negativity P(A) >= 0; normalisation P(Omega) = 1; countable additivity P(union Ai) = sum P(Ai); conditional probability P(A|B) = P(AnB)/P(B) for P(B) greater than 0 — independence P(AnB) = P(A)P(B) — pairwise independence does not imply mutual independence; total probability rule — Bayes Theorem — posterior proportional to likelihood times prior — applications: medical diagnosis — sensitivity; specificity; positive predictive value — base rate neglect fallacy; random variables — discrete: Bernoulli; Binomial B(n,p); Poisson lambda events per interval P(X=k) = exp(-lambda)lambda^k/k! — limit of Binomial; continuous: Normal N(mu,sigma^2); exponential — memoryless property; Gamma; Beta; multivariate — joint; marginal; conditional distributions — covariance; correlation; independence implies zero covariance but converse false; moment generating functions M_X(t) = E[exp(tX)] — uniquely determines distribution; characteristic functions; limit theorems: weak Law of Large Numbers — Chebyshev — sample mean converges in probability to mu; strong LLN — Kolmogorov — almost sure convergence; Central Limit Theorem — Lindeberg-Levy — standardised sample mean converges in distribution to N(0,1) regardless of original distribution — (Xbar - mu)/(sigma/sqrt(n)) converges in distribution to N(0,1) — justifies normal-based inference; Berry-Esseen bound — rate of convergence O(n^{-1/2}); delta method — g(Xbar) approximately Normal(g(mu), [g'(mu)]^2 sigma^2/n) — Taylor expansion; Slutsky's theorem; large deviations — Cramer's theorem — exponential decay of tail probabilities; information theory — Shannon entropy H(X) = -sum P(X=x)log2 P(X=x); Jensen's inequality for convex functions E[g(X)] >= g(E[X]).
Statistical Inference and Decision Theory
Frequentist inference — point estimation methods: method of moments — equate sample moments to population moments; maximum likelihood estimation MLE — maximise log-likelihood l(theta) = sum log f(xi|theta) — score function at MLE equals zero — Fisher information I(theta) = E[-(second derivative of log-likelihood)] — Cramer-Rao lower bound Var(theta-hat) >= 1/I(theta) — MLE achieves CRLB asymptotically; sufficient statistics — Rao-Blackwell theorem — conditioning on sufficient statistic improves estimator; Lehmann-Scheffe — complete sufficient statistic gives UMVUE — uniformly minimum variance unbiased estimator; hypothesis testing — Neyman-Pearson lemma — most powerful test uses likelihood ratio — uniformly most powerful UMP tests for exponential families; significance tests: z-test known variance; t-test unknown variance — Student's t distribution with n-1 df; F-test ratio of variances; chi-squared goodness of fit; likelihood ratio test — Wilks theorem: -2log(Lambda) converges in distribution to chi-squared(k); p-value controversy — Fisher significance testing vs Neyman-Pearson decision theory — incompatible foundations — Jeffreys-Lindley paradox; Bayesian inference — prior distributions — conjugate priors: Beta-Binomial; Gamma-Poisson; Normal-Normal — Jeffreys prior invariant under reparametrisation; posterior inference — credible intervals: probability 0.95 that parameter lies in interval — direct probability statement on parameter unlike frequentist CI; MCMC — Metropolis-Hastings — target distribution pi(theta|x); Gibbs sampler; convergence diagnostics — Gelman-Rubin R-hat; Stan — No-U-Turn Sampler NUTS; hierarchical models — hyperpriors — partial pooling — Gelman Data Analysis Using Regression and Multilevel Hierarchical Models; Bayes factors — direct evidence for H0 vs H1; decision theory — Wald — utility function — Bayes risk — minimax — admissibility — James-Stein estimator — shrinkage dominates MLE for dimensions p >= 3 — Stein's paradox 1956.
Regression and Linear Models
Linear regression — Y = beta0 + beta1*X + epsilon — epsilon ~ N(0,sigma^2) — Gauss-Markov assumptions: E[epsilon|X] = 0; homoscedasticity Var(epsilon) = sigma^2; independence; OLS minimises residual sum of squares RSS — beta-hat = (X'X)^{-1}X'y — Gauss-Markov theorem: OLS is BLUE — Best Linear Unbiased Estimator; inference on beta1 — t-test; confidence bands; prediction intervals wider than CI; R-squared = SSReg/SSTotal proportion of variance explained; multiple regression — multicollinearity — VIF variance inflation factor; model selection — AIC = -2l + 2k; BIC = -2l + k log(n) — LASSO — Tibshirani 1996 — L1 penalty lambda sum |beta_j| — variable selection — shrinkage to zero; Ridge regression L2 penalty lambda sum beta_j^2; Elastic Net; cross-validation — k-fold CV; diagnostics: standardised residuals; Cook's distance — overall influence; heteroscedasticity — Breusch-Pagan test; White robust standard errors; autocorrelation — Durbin-Watson — Newey-West; GLMs — exponential family — link function g(mu) = eta: logistic link for Binomial; log link for Poisson; identity for Normal; logistic regression — binary outcome — log-odds = Xbeta — IRLS; OR = exp(beta); ROC curve; AUC; Hosmer-Lemeshow; Poisson regression — overdispersion — negative binomial; mixed effects models — lme4 R package — REML; non-parametric regression — splines; kernel regression; GAMs — generalised additive models — Wood — mgcv; survival analysis — Cox proportional hazards h(t|x) = h0(t)exp(beta*x) — partial likelihood; hazard ratio HR = exp(beta); Kaplan-Meier estimator; log-rank test.
Multivariate Statistics and Machine Learning
Multivariate analysis — multivariate normal distribution X ~ MVN(mu, Sigma) — Mahalanobis distance d^2 = (x-mu)' Sigma^{-1} (x-mu) ~ chi-squared(p); covariance matrix estimation — sample covariance S; Wishart distribution; principal component analysis PCA — eigendecomposition of Sigma — principal components are uncorrelated linear combinations maximising variance — scree plot; proportion of variance explained; loadings; scores; biplots — dimensionality reduction; noise removal; visualisation; factor analysis — common factor model X = Lambda*f + epsilon — factor loadings; specific variances — rotation: varimax orthogonal; oblimin oblique — exploratory EFA vs confirmatory CFA — structural equation modelling SEM; linear discriminant analysis LDA — Fisher's LDA — maximise between-class variance divided by within-class variance — Mahalanobis distance; QDA; cluster analysis — k-means: minimise within-cluster sum of squares — Lloyd's algorithm; hierarchical: agglomerative — Ward linkage — dendrogram; DBSCAN density-based; silhouette score; t-SNE — Maaten and Hinton high-dimensional visualisation; canonical correlation analysis CCA; machine learning — bias-variance tradeoff: MSE = Bias^2 + Variance + Irreducible Error; CART decision trees — Gini impurity; entropy; random forests — Breiman — bagging plus random feature selection — out-of-bag error — variable importance; gradient boosting — XGBoost; LightGBM; neural networks — universal approximation theorem; SVMs — maximum margin classifier — kernel trick; unsupervised: autoencoders; model evaluation — train/validation/test split; stratified k-fold; regularisation; SHAP — Shapley values — Lundberg and Lee 2017 — feature attribution; causal inference — DAGs — Pearl do-calculus — Rubin potential outcomes — propensity score matching — instrumental variables — difference-in-differences.
Time Series and Stochastic Processes
Time series — weak stationarity: E[Xt] = mu; Cov(Xt,Xt+h) = gamma(h) — depends only on lag h; autocorrelation function ACF; partial autocorrelation PACF; white noise; ARMA models — AR(p): Xt = phi1*Xt-1 + ... + phip*Xt-p + epsilon_t — stationarity: roots of characteristic polynomial outside unit circle; MA(q): Xt = epsilon_t + theta1*epsilon_{t-1} + ... — invertibility; ARMA(p,q); Yule-Walker equations; ARIMA — integrated I(d) — differencing for non-stationarity — Delta*Xt = Xt - Xt-1; Dickey-Fuller unit root test ADF; KPSS test; Box-Jenkins methodology: identification via ACF/PACF; estimation MLE; diagnostic checking Ljung-Box; seasonal SARIMA(p,d,q)x(P,D,Q)_s; spectral analysis — periodogram — discrete Fourier transform — frequency domain — spectral density; state space models — Kalman filter — linear Gaussian — Hamilton — optimal filtering; smoothing; prediction; cointegration — Granger and Engle — two I(1) series with I(0) linear combination — VECM — Johansen test; GARCH — Engle 1982 ARCH; Bollerslev 1986 — sigma_t^2 = omega + alpha*epsilon_{t-1}^2 + beta*sigma_{t-1}^2 — volatility clustering — financial time series; stochastic processes — Markov chains — transition matrix — stationary distribution pi = pi*P; Poisson process — N(t) ~ Poisson(lambda*t) — inter-arrival times exponential — memoryless; Brownian motion — Wiener process W(t) ~ N(0,t) — independent increments — continuous paths; Ito calculus — stochastic differential equations SDE — Ito's lemma; Geometric Brownian Motion — dS = mu*S*dt + sigma*S*dW — Black-Scholes; martingales — optional stopping theorem; Doob's inequality; branching processes — Galton-Watson — extinction probability; queuing theory — M/M/1 queue — utilisation rho = lambda/mu less than 1 for stability.
Biostatistics and Applied Statistics
Clinical trial design — RCT — randomisation — allocation concealment — selection bias prevention; stratified randomisation; blinding: single-blind; double-blind; triple-blind; CONSORT statement 2010 — 25-item checklist; trial phases: Phase I safety and dose-finding; Phase II efficacy signal; Phase III comparative effectiveness; Phase IV post-marketing; sample size calculation — power analysis — alpha = 0.05; power = 0.80 — effect size — G*Power; R pwr package; survival analysis — censoring: right; interval; left truncation — Kaplan-Meier S(t) = product(1 - d_j/n_j) for t_j <= t — log-rank test Mantel-Cox; Cox proportional hazards h(t|x) = h0(t)exp(beta*x) — hazard ratio HR = exp(beta) — proportional hazards assumption — Schoenfeld residuals; meta-analysis — PRISMA — fixed effects pooled estimate; random effects DerSimonian-Laird — tau^2 between-study heterogeneity — I^2: moderate 25-50%; high 75%+; forest plots; funnel plots — Egger's test publication bias; survey statistics — simple random sampling SRS — Horvitz-Thompson estimator; stratified sampling — Neyman optimal allocation; cluster sampling; multistage sampling; design effect DEFF = Var_complex/Var_SRS; survey weighting — calibration; raking; ONS surveys — Understanding Society; Labour Force Survey LFS; missing data — MCAR; MAR; MNAR — Little's test; multiple imputation Rubin 1987 — MICE — van Buuren; observational causal inference — propensity score — Rosenbaum and Rubin 1983 — matching; weighting; stratification; doubly robust estimation AIPW; TMLE — van der Laan; regression discontinuity; difference-in-differences; instrumental variables — Angrist and Pischke Mostly Harmless Econometrics.
Statistics Essay Help — FAQs
Statistics essay writing: (1) Introduction — identify the area: probability theory; mathematical statistics; regression; multivariate methods; time series; Bayesian statistics; applied biostatistics — each has different conventions; state the question precisely — statistics essays require mathematical precision; define notation at the start — let X1,...,Xn be i.i.d. random variables with distribution F, mean mu and variance sigma^2; statistics essays combine mathematical derivation with conceptual explanation — showing you understand why methods work not just how to apply them. (2) Mathematical rigour — state assumptions clearly — the Gauss-Markov theorem holds under specific conditions — violation of assumptions invalidates conclusions; derive key results from first principles where required — do not just state the CLT — show how it follows from the Lindeberg condition; use correct notation throughout — subscripts; superscripts; summation notation; matrix notation — inconsistent notation is a common error; distinguish estimator theta-hat (random variable) from estimate (realised value). (3) Critique methods — what are the assumptions?; how sensitive are results to violation of assumptions?; asymptotic properties — consistency; asymptotic normality; efficiency?; finite-sample issues?; computational considerations — MCMC convergence; numerical instability; what does the method fail to capture? (4) Connect mathematics to applications — the Central Limit Theorem explains why normal distribution is ubiquitous; the bias-variance tradeoff explains why machine learning uses regularisation; Stein's paradox shows MLE is inadmissible in dimensions >= 3. (5) Conclusion — summarise main results; identify open questions; practical implications. Marked on: mathematical correctness; clarity of notation; depth of understanding; critical evaluation.
Top UK Statistics departments: Warwick — Department of Statistics — consistently top-ranked — probability; Bayesian computation; machine learning; financial statistics; CRiSM Centre for Research in Statistical Methodology; Oxford — Department of Statistics — statistical machine learning; computational statistics; Bayesian methods; Cambridge — Statistical Laboratory Faculty of Mathematics — probability theory; statistical genetics; genomics; LSE — Statistics — methodology; economics applications; social statistics; Imperial College London — Mathematics — statistical genetics; bioinformatics; UCL — Statistical Science — bioinformatics; genetics; medical statistics; Edinburgh — MSc Statistics with Data Science; Bristol — Bayesian statistics; environmental statistics; Sheffield — MASH Maths and Statistics Help. Professional bodies: Royal Statistical Society RSS — Fellow FRS — Graduate GradStat — Chartered Statistician CStat — publications: JRSS-A; B; C — RSSB highest prestige for methodology; Statistics in Medicine; Biometrics; APTS — Academy for PhD Training in Statistics — national graduate training; EPSRC statistics funding; NIHR statistics biomedical statistics.
Central Limit Theorem essay explanation: (1) Statement — Lindeberg-Levy CLT: Let X1,...,Xn be i.i.d. with mean mu and variance sigma^2 less than infinity. Then as n tends to infinity: sqrt(n)(Xbar_n - mu)/sigma converges in distribution to N(0,1). Equivalently Xbar_n is approximately N(mu, sigma^2/n) for large n — regardless of original distribution of Xi. (2) Why it matters — explains ubiquity of normal distribution: physical measurements are sums of many small independent errors — Gauss 1809; biological traits like height determined by many independent genetic and environmental factors; financial returns are aggregates of many small price movements; justifies normal-based inference for large samples. (3) Generalisations — Lindeberg condition: no single variable dominates the sum; Lyapunov condition sufficient and easier to verify; multivariate CLT: sqrt(n)(Xbar_n - mu) converges in distribution to MVN(0, Sigma); Donsker's theorem functional CLT — scaled random walk converges to Brownian motion. (4) Rate of convergence — Berry-Esseen bound — rate O(n^{-1/2}) — slow for skewed distributions; Edgeworth expansions — higher-order corrections involving skewness and kurtosis; bootstrap — non-parametric alternative especially useful when n is small. (5) Practical — n >= 30 rule of thumb not universal: symmetric near-normal n=5 may suffice; heavy-tailed n=100+ needed; limitations: CLT requires finite variance — Cauchy distribution has no finite variance — does not satisfy CLT; stable distributions — generalised CLT; extreme value distributions — maximum of i.i.d. sequence — Fisher-Tippett-Gnedenko theorem.
Statistics essay referencing — APA 7th or author-year (common in statistics): Journal article: Cox, D. R. (1972). Regression models and life-tables. Journal of the Royal Statistical Society Series B, 34(2), 187-202. Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. JRSS-B, 58(1), 267-288. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5-32. Book: Casella, G., and Berger, R. L. (2002). Statistical inference (2nd ed.). Duxbury. Gelman, A., et al. (2013). Bayesian data analysis (3rd ed.). CRC Press. Lehmann, E. L., and Romano, J. P. (2005). Testing statistical hypotheses (3rd ed.). Springer. Key statistics journals: Journal of the Royal Statistical Society Series B — JRSS-B — methods — highest prestige; Annals of Statistics — AoS — IMS — theoretical; Biometrika — Oxford — founded 1901 — Karl Pearson; JASA Journal of the American Statistical Association — applied and methods; Statistical Science — IMS — expository; Annals of Probability — probability theory; Bernoulli — probability and statistics; Biometrics — clinical and biological; Statistics in Medicine; Bayesian Analysis — ISBA; Journal of Computational and Graphical Statistics. Key textbooks: probability: Billingsley Probability and Measure; Williams Probability with Martingales; Durrett Probability Theory and Examples; inference: Casella and Berger Statistical Inference; Lehmann and Romano Testing Statistical Hypotheses; regression: Faraway Linear Models with R; McCullagh and Nelder Generalized Linear Models; Bayesian: Gelman et al BDA3; machine learning: Hastie Tibshirani Friedman Elements of Statistical Learning; Bishop Pattern Recognition and Machine Learning.
Bayesian vs frequentist essay framework: (1) Philosophical foundations — frequentist: probability is long-run frequency — P(A) = lim n_A/n — parameters are fixed unknown constants — inference is about repeated sampling properties: unbiasedness; consistency; 95% CI has 95% coverage in repeated sampling; Bayesian: probability is degree of belief — parameters are random variables — Bayes Theorem updates prior beliefs with data — posterior = likelihood times prior divided by evidence — P(H|E) = P(E|H)P(H)/P(E) — direct probability statement on parameter — de Finetti subjective probability — Dutch book argument. (2) Key differences — confidence vs credible intervals: frequentist 95% CI: in repeated sampling 95% contain true theta — cannot say P(theta in CI) = 0.95; Bayesian 95% credible interval: P(theta in CI | data) = 0.95 — intuitive direct statement; hypothesis testing: frequentist p-value vs Bayesian Bayes factors BF = P(data|H0)/P(data|H1) — direct evidence for H0 vs H1; multiple comparisons: frequentist FWER and FDR corrections; Bayesian: hierarchical models — partial pooling — natural shrinkage. (3) Prior sensitivity — Jeffreys prior invariant under reparametrisation; weakly informative priors — Gelman; regularisation connection: Ridge regression = Bayesian with Normal prior; LASSO = Laplace prior; Bayesian neural networks — weight uncertainty. (4) Computational — frequentist: OLS; MLE — analytical solutions; Bayesian: MCMC required — convergence diagnostics; variational Bayes — approximate faster inference. (5) Reconciliation — Bernstein-von Mises: posterior concentrates around MLE asymptotically; empirical Bayes — Efron — estimate prior from data; pragmatic: Bayesian for hierarchical models; incorporating prior knowledge; predictions; frequentist for controlled experiments with pre-specified tests; regulatory contexts requiring objectivity; Gelman Bayesian workflow — iterative model building; checking; revision.
Replication crisis essay framework: (1) What is it — Ioannidis 2005 Why Most Published Research Findings Are False — PLOS Medicine — statistically underpowered studies with publication bias produce majority of false positives; Open Science Collaboration 2015 Reproducibility Project Psychology — replicated 100 psychology studies — only 36-39% replicated — Nature; Begley and Ellis 2012 Nature — 53 landmark cancer papers could not be replicated; reproducibility vs replicability: reproducibility — same data same analysis — computational; replicability — new data same hypothesis. (2) Statistical causes — publication bias: positive results more likely published — file drawer problem — Rosenthal 1979 — funnel plot asymmetry — Egger's test; p-hacking: Simmons Nelson Simonsohn 2011 False-Positive Psychology — researcher degrees of freedom — HARKing — Hypothesising After Results are Known; underpowered studies: Cohen 1962 — mean power of psychology studies 0.48; inflated effect size estimates — winner's curse; multiple comparison without correction. (3) Solutions — pre-registration: OSF Open Science Framework; AsPredicted — distinguishes confirmatory from exploratory; Registered Reports: journals peer-review Methods before data collection — acceptance conditional on design not results; power analysis: G*Power prospective not post-hoc; effect size reporting: Cohen's d; r; OR — not just p less than 0.05; meta-analysis — cumulate evidence — reduce sampling error; open data and code: FAIR principles — Findable Accessible Interoperable Reusable; UKRN UK Reproducibility Network; ASA statement 2016 — Wasserstein and Lazar — p-value not measure of effect size; Moving to a world beyond p less than 0.05 — 2019; Bayes factors; estimation approach — Cumming New Statistics; equivalence testing TOST; replication incentives — tenure reform; statistical training reform.
Related Subject Areas
Statistics — Bayesian, Frequentist, Applied
UK statistics (LSE, Warwick, Edinburgh, UCL Stats Science) expects theoretical foundations and applied competence in R, Python, Stata, SAS. Writers hold MSc/PhD in Statistics or Biostatistics, several with RSS-fellow status.
Assignment Types
- Hypothesis testing, ANOVA, regression diagnostics
- GLMs and mixed models in R lme4
- Bayesian inference (Stan, PyMC); time-series (Box-Jenkins, GARCH)
- Survival analysis (Cox PH); ML in scikit-learn
Reference Texts
- Casella & Berger; Wasserman All of Statistics
- Gelman et al BDA; Hastie-Tibshirani-Friedman ESL
FAQ
Statistical chapter for non-stats dissertations?
Yes — methods, results, interpretation chapters for psychology, sociology, business, health sciences.
