Junior Statistician Qualification Examination study guide
Junior Statistician Qualification Examination · Work through your exam, one topic at a time.
The Junior Statistician Qualification Examination (JSQE) is a professional credential for early-career statisticians and data analysts who want independent verification of foundational statistical competence. It suits graduates entering applied roles, technicians moving into analysis, and candidates preparing for more advanced certifications. The Junior Statistician Qualification Examination study guide has 9 sections and about 4,184 words.
01Descriptive Statistics and Data Presentation
This domain checks whether you can summarise a data set correctly and display it without distortion. Items cover measurement scales, measures of centre and spread, distribution shape, and the construction and reading of frequency tables, histograms, boxplots and ogives.
492 words, about 2 min02Probability Theory and Distributions
This domain tests the rules that turn uncertain information into numbers: the probability axioms, conditional probability and Bayes, random variables with their expectations, and the standard distributions that later feed inference. Three axioms: probability is non-negative; the sample space has probability 1; for disjoint events the probability of the union is the sum of the probabilities.
479 words, about 2 min03Statistical Inference and Estimation
This domain covers how a sample is used to estimate a population quantity: the properties that make an estimator trustworthy, standard errors, confidence intervals, and the sample size needed to reach a stated precision. Unbiased: the expected value of the estimator equals the parameter.
486 words, about 2 min04Hypothesis Testing
This domain tests whether a claim about a population is supported by sample evidence. Expect items on setting up hypotheses, choosing the correct test statistic, interpreting p-values, and understanding the trade-off between error rates and power.
502 words, about 3 min05Regression and Correlation
This domain examines how two measured variables move together and how one can be predicted from the other: correlation coefficients, the least-squares line, diagnostics, and the assumptions that make inference on a slope valid. Pearson r equals the covariance divided by the product of the standard deviations; it lies between minus 1 and 1 and is unit-free.
427 words, about 2 min06Experimental Design and Sampling
This domain covers how data are generated: the principles that make an experiment informative and the schemes that make a sample representative of the population you want to describe. Randomisation of treatments protects against confounding by unmeasured variables.
426 words, about 2 min07Nonparametric Methods and Categorical Data
This domain covers analyses that do not rely on a normal distribution: rank-based tests for location and association, and chi-square procedures for counts in categories. Use them when data are ordinal, samples are small with clearly non-normal shape, or outliers dominate the mean.
446 words, about 2 min08Time Series and Index Numbers
This domain covers data observed over time and the construction of index numbers that track price or quantity change. Expect items on decomposition, smoothing, and the choice among index formulas and their known biases.
466 words, about 2 min09Statistical Computing and Data Handling
This domain tests the practical craft of getting data into an analysis without corrupting it: data types, cleaning, missing values, reproducibility, and the numerical and indexing habits that cause silent errors. Know your types: numeric, integer, character, factor or categorical, and date-time.
460 words, about 2 min