“150”, Aptitude Test Questions and Answers for Statistician II – The Office of Treasury Registrar (OTR).
ABSTRACT
This work presents 150 multiple-choice
questions with answers and detailed rationales to prepare candidates for the Statistician
II aptitude test at the Office of the Treasury Registrar (OTR), Tanzania.
The questions cover data collection, analysis, interpretation, forecasting,
reporting, and risk identification, reflecting core job duties. Each question
is designed with closely related options to mirror the real test environment,
helping candidates strengthen analytical skills, improve decision-making, and
build confidence for exam success.
Prepared by:
Research Officer
Compiled by Johnson Yesaya Mgelwa.
A lawyer stationed in Dar-es-salaam.
0628729934.
Date: September 1, 2025
Dear applicants,
This collection of questions and answers
has been prepared to help all of you to understand the key areas tested during
the aptitude test. The goal is to provide a useful, and practical study guide
so you can all perform confidently and fairly in the selection process. I wish
you the best of luck, and may this resource support you in achieving success!
Warm regards,
Johnson Yesaya Mgelwa
For Personal Use by Applicants Preparing
for Research Officer II – The Office of Treasury Registrar interview.
ALL
QUESTIONS TOGETHER.
1. What is the primary purpose of
inferential statistics in research?
A. Summarizing collected data B. Making predictions about populations C.
Collecting raw data D. Creating data visualizations
Answer: B
Rationale: Inferential statistics allow
statisticians to make generalizations or predictions about a population based
on a sample. Unlike descriptive statistics, which summarize data, inferential
methods estimate parameters, test hypotheses, and forecast trends, which is
critical for decision-making.
2. Which of the following best represents a
continuous variable?
A. Gender B. Age in years C. Marital status D. Type of car
Answer: B
Rationale: A continuous variable can take an
infinite number of values within a range. Age in years can vary continuously
(e.g., 25.4 years), while gender, marital status, and car type are categorical
variables with discrete values.
3. If a dataset has extreme values
(outliers), which measure of central tendency is least affected?
A. Mean B. Mode C. Median D. Standard deviation
Answer: C
Rationale: The median represents the middle value of
a sorted dataset and is resistant to extreme values, whereas the mean can be
heavily skewed by outliers. Mode is also less sensitive but may not reflect
central tendency in continuous data.
4. A researcher wants to determine whether
there is a relationship between hours studied and exam scores. Which
statistical method is most appropriate?
A. Chi-square test B. Correlation analysis C. ANOVA D. Logistic regression
Answer: B
Rationale: Correlation analysis measures the
strength and direction of the relationship between two continuous variables.
Hours studied and exam scores are numeric, making correlation the appropriate
choice.
5. What does a p-value less than 0.05 in
hypothesis testing indicate?
A. Strong evidence against the null hypothesis B. No relationship exists C.
Data are invalid D. Sample size is too small
Answer: A
Rationale: A p-value < 0.05 indicates that the
observed data are unlikely under the null hypothesis, providing strong evidence
to reject it. It is a standard threshold to determine statistical significance
in research.
6. In data analysis, what is the main
function of a histogram?
A. Showing exact data points B. Displaying frequency distribution C. Testing
correlation D. Forecasting trends
Answer: B
Rationale: Histograms graphically display the
frequency distribution of continuous or discrete data. They help identify
patterns, such as skewness, symmetry, or outliers, but do not test correlations
or make predictions.
7. Which method is most appropriate to
forecast future values from past numerical data?
A. Linear regression B. Chi-square test C. ANOVA D. Factor analysis
Answer: A
Rationale: Linear regression uses historical data to
model relationships between variables and predict future outcomes. It is widely
used in trend forecasting, while chi-square, ANOVA, and factor analysis serve
other statistical purposes.
8. Which of the following is a measure of
variability in a dataset?
A. Mean B. Median C. Standard deviation D. Mode
Answer: C
Rationale: Standard deviation quantifies how much
data values deviate from the mean, indicating variability. Mean, median, and
mode measure central tendency, not dispersion.
9. In a research report, why is it important
to identify relationships between variables?
A. To ignore irrelevant data B. To predict outcomes and inform decisions C. To
collect more data D. To create data entry forms
Answer: B
Rationale: Identifying relationships allows analysts
to understand how one variable affects another, forecast trends, and guide
decision-making. It is central to effective data-driven policy and planning.
10. Which technique is used to reduce data
dimensionality while retaining most information?
A. Factor analysis B. T-test C. ANOVA D. Regression
Answer: A
Rationale: Factor analysis identifies underlying
latent variables and reduces the number of observed variables without losing
significant information, unlike t-tests, ANOVA, or regression, which focus on
relationships or differences.
11. When monitoring data collected over time,
what is the primary purpose?
A. Delete irrelevant data B. Publish reports only C. Limit sample size D.
Ensure accuracy and consistency
Answer: D
Rationale: Monitoring ensures that data maintain
quality throughout its lifecycle. Consistent and accurate data allow reliable
analysis and informed decision-making, essential for statistical work in
institutions like OTR.
12. If a survey produces data from multiple
categories that do not have a natural order, what type of variable is this?
A. Ordinal B. Continuous C. Interval D.
Nominal
Answer: D
Rationale: Nominal variables classify data into
distinct categories without inherent order (e.g., colors, departments). Ordinal
variables have a meaningful order, and continuous/interval variables are
numeric.
13. Which statistical method helps detect
trends in public expenditure over several years?
A. Time series analysis B. Cross-sectional analysis C. Chi-square test D.
Factor analysis
Answer: A
Rationale: Time series analysis evaluates data
collected sequentially over time, identifying trends, cycles, or seasonal
effects. Cross-sectional analysis examines a single point in time, while
chi-square and factor analysis serve different purposes.
14. A dataset contains 100 values. To
summarize the typical value, which statistic is best if the data are highly
skewed?
A. Mean B. Median C. Mode D. Range
Answer: B
Rationale: The median is the best measure of central
tendency for skewed data because it is not influenced by extreme values,
providing a better representation of a typical value than the mean or mode.
15. What is the main goal of biostatistics in
public institutions?
A. Designing buildings B. Filing paperwork C. Supervising staff D. Analyzing biological data to inform policy
Answer: D
Rationale: Biostatistics applies statistical methods
to biological and health data, enabling evidence-based decisions in public
policy, research, and resource allocation, aligning with duties of
statisticians in government.
16. In a forecast of public expenditure, what
is the risk if data quality is poor?
A. Accurate predictions B. Misleading trends and decisions C. Faster
computation D. Larger sample sizes
Answer: B
Rationale: Poor-quality data lead to inaccurate
models, which can result in misleading trends, misallocation of resources, and
poor policy decisions. Monitoring and cleaning data are essential for reliable
forecasting.
17. Which of the following describes a null
hypothesis?
A. A statement to be tested for possible rejection B. The research conclusion C.
A summary of findings D. A type of data visualization
Answer: A
Rationale: The null hypothesis is a default
assumption stating that there is no effect or relationship. It is tested
statistically, and rejection of the null provides evidence for the alternative
hypothesis.
18. A statistician notices unusual patterns
in a dataset. What is the first step?
A. Ignore the patterns B. Publish immediately C. Investigate potential errors
or anomalies D. Delete all data
Answer: C
Rationale: Unusual patterns may indicate errors,
outliers, or important findings. Investigating ensures data integrity and
accurate interpretation before drawing conclusions or reporting.
19. Which method is best for analyzing
categorical variables in a contingency table?
A. Linear regression B. Time series C. ANOVA
D. Chi-square test
Answer: D
Rationale: The chi-square test evaluates
associations between categorical variables in contingency tables. It determines
whether observed frequencies differ significantly from expected frequencies.
20. What is a primary responsibility of a
Statistician II regarding reports?
A. Collecting signatures B. Preparing summaries and statistical analyses C.
Filing forms only D. Supervising other departments
Answer: B
Rationale: Preparing summaries and reports of
statistical analyses allows management to interpret data and make informed
decisions. This aligns with the key duties of the Statistician II position.
21. Which of the following best explains
standard error?
A. Variation in a population B. Variation in a sample mean C. Maximum value in
dataset D. Median value
Answer: B
Rationale: Standard error measures the variability
of a sample means relative to the population mean. It indicates the precision
of the sample estimate, unlike population variance or median, which have
different purposes.
22. In regression analysis, what does the
coefficient indicate?
A. Data entry method B. Sample size C. The strength and direction of
relationship between variables D. P-value only
Answer: C
Rationale: The regression coefficient quantifies how
much the dependent variable changes with a unit change in the independent
variable, indicating both strength and direction of the relationship.
23. When forecasting public expenditure,
which type of error is most critical to minimize?
A. Typographical error in reports B. Spelling error in charts C. Measurement error in past data D. Sample
size notation
Answer: C
Rationale: Measurement errors in historical data
distort the forecasting model, leading to inaccurate predictions and poor
policy decisions. Correct data collection and validation are essential to
minimize these errors.
24. What is the primary advantage of using
summary tables in statistical reports?
A. Hiding data B. Presenting key patterns clearly C. Increasing page count D.
Replacing graphs entirely
Answer: B
Rationale: Summary tables condense complex datasets
into digestible formats, highlighting key patterns and trends. They allow
decision-makers to interpret data efficiently, supporting informed choices.
25. Which of the following is a reason for
carrying out data monitoring throughout its lifecycle?
A. To ensure reliability and timely decision-making B. To avoid analysis C. To
reduce sample size D. To prevent data entry
Answer: A
Rationale: Monitoring ensures data remain accurate,
consistent, and relevant, enabling informed decision-making. Neglecting this
can result in errors, misleading interpretations, or incorrect policy actions.
26. Which of the following best describes a
population in statistics?
A. A single observation B. The entire set of subjects of interest C. A random
sample D. The mean value
Answer: B
Rationale: In statistics, a population includes all
subjects or elements under study. A sample is a subset of the population, while
single observations or means represent data points or summaries.
27. If two variables are inversely related,
what does this imply?
A. Both increase together B. One increases as the other decreases C. No
relationship exists D. Variables are categorical
Answer: B
Rationale: An inverse relationship indicates that as
one variable rises, the other falls. Recognizing such relationships is
essential for forecasting and interpreting trends in statistical work.
28. Which type of graph is most suitable for
showing proportions of categories in a dataset?
A. Line graph B. Pie chart C. Scatter plot D. Histogram
Answer: B
Rationale: Pie charts visually display proportions
of categories within a dataset, making it easy to interpret relative
contributions. Histograms and line graphs serve different purposes, while
scatter plots show relationships between variables.
29. When performing hypothesis testing, what
does a Type I error mean?
A. Accepting a false null hypothesis B. Rejecting a true null hypothesis C.
Accepting a true null hypothesis D. Rejecting a false null hypothesis
Answer: B
Rationale: A Type I error occurs when the null
hypothesis is true but is incorrectly rejected. This represents a false
positive, which can mislead conclusions in statistical analyses.
30. Which method helps in identifying factors
affecting public expenditure trends?
A. Regression analysis B. ANOVA C. Chi-square test D. Median calculation
Answer: A
Rationale: Regression analysis quantifies how independent variables influence a dependent variable, helping to identify significant factors affecting trends such as public expenditure.
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