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Quizzes > Quizzes for Business > Education

Science Trust and Literacy Assessment Quiz

Test Your Science Literacy and Trust Skills

Difficulty: Moderate
Questions: 20
Learning OutcomesStudy Material
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This Science Trust and Literacy Assessment Quiz helps you judge scientific claims and read data with care. Answer 15 quick questions to see what you do well and where you can improve. When you finish, try the related knowledge quiz or review terms with vocabulary practice .

Which type of source is generally considered most credible for scientific information?
Personal blog post
Editorial in a newspaper
Social media post
Peer-reviewed journal article
Peer-reviewed articles undergo evaluation by independent experts, which helps ensure the validity and reliability of the research. Blog posts, social media, and editorials lack systematic external review and are more prone to error or bias.
What defines a primary scientific source?
A summary of research findings by journalists
An original research report published by the researchers themselves
An editorial opinion on scientific topics
A textbook chapter explaining established theories
Primary sources present original data and methods directly from the researchers who conducted the study. Textbooks, editorials, and news articles are secondary or tertiary interpretations of that original work.
The term "peer-reviewed" indicates which of the following?
It was self-published by the author
It was reviewed only by the journal's editor
The author paid a fee to publish the work
The work was evaluated by independent experts before publication
Peer review means that independent experts in the field assessed the research for validity and quality before publication. Editorial-only or self-published work does not guarantee the same level of external scrutiny.
Which best describes evidence-based reasoning?
Drawing conclusions based on systematically collected data
Trusting intuition over data
Basing conclusions solely on authority
Following popular opinion
Evidence-based reasoning relies on data collected through systematic methods and objective analysis. Intuition, popularity, or authority alone do not ensure conclusions are supported by reliable evidence.
What does confirmation bias refer to?
Adjusting data to fit statistical models
The tendency to favor information that supports existing beliefs
Ignoring all evidence when making decisions
The habit of seeking conflicting viewpoints
Confirmation bias is the tendency to search for, interpret, and remember information that confirms one's preconceptions. It leads to ignoring or undervaluing evidence that contradicts existing beliefs.
What problem arises when a graph uses a truncated y-axis to exaggerate differences?
Selection bias in data collection
Confirmation bias in interpretation
Misleading visual representation due to axis manipulation
Random sampling error
Truncating the y-axis can exaggerate or minimize differences visually, misleading viewers about the true magnitude of change. This manipulation does not involve sampling or selection bias but distorts perception.
Which error occurs when concluding causation from correlation?
Confounding multiple variables
Ignoring random variation
Using anecdotal evidence instead of data
Assuming that because two variables change together, one causes the other
Correlation does not imply causation; two variables may change together due to a third factor or coincidence. True causation requires controlled experiments or additional evidence to confirm a direct link.
What term describes selecting only studies that support a hypothesis and ignoring others?
Randomization
Double-blinding
Cherry-picking
Systematic review
Cherry-picking involves focusing on favorable studies while disregarding contradictory evidence, leading to biased conclusions. Systematic reviews aim to include all relevant studies, and randomization and blinding are experimental safeguards.
A scientist's study is funded by an industry with vested interests. Which bias is most relevant?
Funding bias
Placebo effect
Sampling bias
Observer bias
Funding bias occurs when research outcomes are influenced by the sponsor's interests, potentially skewing results. Sampling and observer biases arise from data collection and interpretation methods rather than funding sources.
How does sample size affect the reliability of lab report findings?
Larger samples always guarantee correct conclusions
Larger sample sizes generally increase statistical power
Sample size has no impact on variability
Smaller samples reduce measurement error
Larger sample sizes improve the ability to detect true effects by reducing random error and increasing statistical power. They do not automatically guarantee correct conclusions, but they do make results more reliable than very small samples.
Which practice involves manipulating data until statistical significance is found?
Blinding
Replication
Random sampling
P-hacking
P-hacking refers to selectively testing or transforming data until p-values meet the significance threshold, risking false positives. Replication, random sampling, and blinding are methods that strengthen study validity, not undermine it.
Which statement shows proper conflict-of-interest disclosure?
Only disclose financial ties if asked
No disclosure is needed for publicly funded work
The researchers declare all funding sources and affiliations
The lead author recommends ignoring funding details
Proper disclosure lists all financial and affiliation ties so readers can assess potential influence. Omitting or downplaying funding details prevents transparent evaluation of bias risks.
How does a systematic review differ from anecdotal evidence?
It follows a predefined protocol to analyze multiple studies objectively
It interprets data based on a researcher's intuition
It lacks citations and references
It relies on single personal experiences
Systematic reviews use structured methods to gather and synthesize all relevant research, minimizing bias. Anecdotes are individual cases that cannot represent the broader evidence base and lack methodological rigor.
What distinguishes statistical significance from practical significance?
Practical significance depends solely on p-values
Statistical significance relates to p-values; practical significance concerns real-world impact
They are two terms for the same concept
Statistical significance always implies large effect size
Statistical significance indicates the likelihood an effect is not due to chance, while practical significance evaluates whether the effect size is meaningful in real-world contexts. A small effect can be statistically significant but practically negligible.
Which strategy helps verify the credibility of online scientific claims?
Relying solely on social media endorsements
Trusting the first search result without checking sources
Consulting reputable academic databases such as PubMed
Accepting information that aligns with personal beliefs
Academic databases like PubMed index peer-reviewed literature, ensuring access to vetted research. Social media, first-page results, and personal biases do not guarantee accuracy or objectivity.
In neuroimaging studies, what is the reverse inference problem?
Only analyzing data from one brain region
Scanning subjects without informed consent
Using imaging data without any statistical tests
Inferring specific cognitive processes solely from observed brain activation
Reverse inference assumes that activation in a brain area conclusively indicates a particular mental process, which is invalid because regions can serve multiple functions. Proper inference requires converging evidence beyond activation alone.
Which methodological flaw most undermines the validity of a new drug efficacy experiment?
Using both male and female participants
Absence of a randomized placebo control group
Reporting both positive and negative results
Measuring outcomes at multiple time points
Without a randomized placebo control, it's impossible to separate the drug's effect from placebo effects or other confounders. Proper controls and randomization ensure valid causal attribution.
Which cognitive bias leads researchers to interpret ambiguous data in line with their expectations?
Sampling bias
Expectancy bias
Observer effect
Publication bias
Expectancy bias occurs when researchers' expectations influence how data are interpreted or recorded, especially if results are ambiguous. Other biases relate to sampling, measurement, or publication practices rather than interpretation.
High heterogeneity (I^2 > 75%) in a meta-analysis indicates what?
The meta-analysis has no statistical significance
Substantial variability among study results, warranting cautious interpretation
All studies perfectly agree
There is no need for subgroup analysis
High I^2 values reflect considerable inconsistency across study outcomes, suggesting that pooling may mask important differences. Careful subgroup or sensitivity analyses are needed to explore sources of variability.
Which feature is characteristic of a predatory journal?
Providing transparent editorial board information
Being indexed in major academic databases
Implementing rigorous peer-review standards
Charging publication fees without offering proper peer review
Predatory journals solicit authors for fees but fail to conduct genuine peer review or maintain editorial standards. Legitimate journals are typically indexed, transparent, and uphold thorough peer-review processes.
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Learning Outcomes

  1. Analyse credibility of scientific sources and claims.
  2. Evaluate data interpretations in lab reports.
  3. Identify biases affecting scientific information.
  4. Apply critical thinking to research findings.
  5. Demonstrate understanding of evidence-based reasoning.
  6. Master strategies for discerning trustworthy science content.

Cheat Sheet

  1. Understand the CRAAP Test - The CRAAP Test is like your detective kit for sources: check Currency, Relevance, Authority, Accuracy, and Purpose to sniff out unreliable info. By running through these five checkpoints, you'll quickly decide if an article is solid or suspect.
  2. Recognize Confirmation Bias - We all love facts that hug our beliefs, but confirmation bias can turn that love into a trap. Challenge your assumptions by actively seeking opposing perspectives and playing devil's advocate.
  3. Assess Source Authority - Before trusting what you read, double-check the author's credentials, affiliations, and past work. Major universities and respected journals usually carry more weight than random blogs.
  4. Evaluate Data Interpretation - Numbers can be twisted and charts can lie if not handled properly. Always ask if the data presentation matches the conclusions by checking for omitted context or misleading scales.
  5. Identify Purpose and Bias - Every piece of info comes with a motive: inform, persuade, entertain, or sell! Asking who benefits if you accept its claims helps you spot hidden agendas.
  6. Check for Peer Review - Peer-reviewed studies have passed expert scrutiny, which is your green light for reliability. Articles in reputable journals usually undergo this process - so prioritize them in your research.
  7. Analyze Research Methods - Solid conclusions start with solid methods: clear hypotheses, proper controls, and suitable sample sizes. Methodology details tell you how trustworthy the findings really are.
  8. Understand Statistical Significance - Terms like p-values and confidence intervals show how likely results are due to chance. A p-value below 0.05 often indicates strong evidence, while confidence intervals reveal result precision.
  9. Recognize Logical Fallacies - Watch out for reasoning traps like hasty generalizations or false causality that can undermine arguments. Spotting these keeps your critical-thinking skills razor-sharp.
  10. Stay Updated - Science is a moving target - today's fact can become tomorrow's footnote. Make it a habit to revisit your topics regularly and subscribe to trusted journals or newsletters to keep your knowledge fresh.
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