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Exploratory Data Analysis test for hiring

See how candidates interpret a new dataset and prepare its raw columns for modeling.

10 minuten
15 vragen
Intermediate
Multiple choice
Questions in Engels
Phone and desktop

Use this test free See the question formats

Het korte antwoord

The Exploratory Data Analysis test is an intermediate, 10-minute hiring assessment with 15 multiple-choice questions. Candidates read small tables and charts, judge what the numbers support, and decide how to clean, transform, or combine raw columns. It is designed for data analysts, data scientists, data engineers, and other roles that explore datasets.

Wat het meet

Reading and summarizing data with statistics and charts, and preparing features from raw columns.

Statistical summaries

Candidates read summary tables and use statistics to describe data and judge what the numbers support.

Reading and choosing charts

Candidates interpret charts and choose a chart that helps represent the data.

Preparing features from raw data

Candidates decide how to clean, transform, or combine raw columns into features before modeling.

Question formats

Multiple choice

The candidate: Reads a short situation, table or statement and picks the right answer, or every right answer.

You see: Whether the candidate knows the material well enough to apply it, not just recognize it.

15 questions in 10 minutes, served one at a time.

Roles this test fits

Data analysts, data scientists, data engineers, business and product analysts, and other roles that explore datasets to find patterns and prepare them for modeling.

What this test can show

Reason from data in view
Each question includes a small table or chart, so you can review how candidates interpret specific data rather than rely on definitions.
What the numbers do and do not support
Candidates judge which conclusions a summary table or chart supports, which shows whether they would overstate a pattern in a first look at new data.
Use accessible scenarios
Everyday datasets, plain wording, light arithmetic, and charts that do not rely on color alone keep the focus on data reasoning.

What you get for each candidate

Example report (fictional data)Exploratory Data Analysis

68%Percentile 40. Benchmark: HireValid candidates, n = 1.006

Statistical summaries58%

Reading and choosing charts73%

Preparing features from raw data72%

Integrity ScoreHoge betrouwbaarheid

Stayed on the test tab throughout
No pasted answers
Answered at a steady pace

Scores, profiles and integrity signals are evidence for you to review alongside an interview, never an automatic decision.

Use this test free

Controls and review signals

Each candidate receives a different set of questions from large question pools. Question order and answer options are shuffled. The browser shows one question at a time, and answer keys stay on HireValid's servers. Timing runs on the server; reloading restores the same question with only the time remaining, and candidates cannot go back. Question text cannot be copied or selected, and a blocked copy attempt is recorded in the timeline. HireValid records signals such as time away from the test tab, pastes into answers, and answers faster than a question can be read. An Integrity Score and evidence timeline summarize signals for employer review; the score never decides on its own. With candidate consent, employers can add webcam photos at random moments.

Hoe we fraude met AI tegengaan · Integrity signals

Use the score as evidence

Review the score alongside a work sample that asks candidates to explain a chart or prepare raw columns. Then use a structured interview to explore their reasoning. Consider the Data Science test or Data Extraction, Transformation, and Loading (ETL) for related hiring evidence. The score is one input for a person to review, not a stand-alone decision.

Vragen voor gestructureerde sollicitatiegesprekken

What would you check first in a new dataset of website sessions?
Listen for a clear first look at summary data and charts, and a distinction between what the numbers show and what they cannot establish.
How would you decide whether to transform or combine two raw columns before modeling?
Listen for a reasoned preparation choice tied to the columns and the intended use, rather than a memorized definition.
How would you choose a chart to explain a pattern in warehouse stock data?
Listen for a choice that fits the data and an explanation of what the chart does and does not communicate.

Understand scoring and benchmarks

Fairness and accommodations

Datasets come from everyday settings such as deliveries, clinic visits, website sessions, energy use and warehouse stock, with metric units, no money symbols, sports, holidays or local knowledge, and names from many backgrounds. Charts never rely on color alone: series are told apart by labels, markers or line styles. Arithmetic stays light enough for scratch paper, no code is shown, and the wording is plain so the test measures data reasoning rather than reading speed.

You can give any candidate extra time as an accommodation. Results are evidence for a person to review, never an automatic decision.

Provisional: there are no norms yet, and reliability is reported once 200 candidates have completed it.

Lees hoe we met eerlijkheid omgaan

Test details

A short, timed test of the first look an analyst takes at a new dataset. Candidates read summary tables and charts, judge what the numbers do and do not support, and decide how raw columns should be cleaned, transformed or combined before modeling. Every question shows its own small table or chart, so the work is reading the data in front of them rather than recalling definitions.

Questions are in English.
Aids allowed: Scratch paper.
Each question is answered once, in order, with no going back.
4 practice questions, 4 minutes, before the timed test.

Veelgestelde vragen

Who should take the Exploratory Data Analysis test?+

It is for intermediate hiring in data analyst, data scientist, data engineer, business analyst, and product analyst roles, plus other work exploring datasets for patterns and preparing them for modeling.

How does the Exploratory Data Analysis test handle fairness?+

Scenarios use everyday settings such as deliveries, clinic visits, and website sessions, with metric units and names from many backgrounds. Charts distinguish series through labels, markers, or line styles, not color alone.

Can candidates use scratch paper on the Exploratory Data Analysis test?+

Yes. Scratch paper is allowed, and the arithmetic stays light enough for it. Candidates can also try a practice set of four questions in four minutes before taking the 15-question test.

Does the Exploratory Data Analysis test include code?+

No. No code is shown, so no one tool or programming language gives a candidate an advantage. Questions focus on reading summaries and charts and on deciding how raw columns should be cleaned, transformed or combined.