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flashcard
Stratified / Randomised / Confounding / Margin of Error
Flashcards for key sampling and bias terms.
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Identify sampling methodologies and recognize how confounding variables and bias compromise research validity.
Flashcards for key sampling and bias terms.
Introduction to sampling methods, confounding variables, and errors.
Imagine testing a massive pot of biryani. You don't need to eat the whole pot to know if it needs salt; you just need one spoonful. But what if you only scooped from the very top, missing the spices at the bottom? That spoonful is your sample. If your sampling method is flawed, your conclusions will be completely wrong. Bad sampling inevitably leads to bad research, no matter how complex your math is.
Conceptual metaphor doodles for sampling and bias.
Clean scientific diagram showing a layered cake with different colored tiers, with a spoon taking a perfectly vertical s…
Professional diagram of a bingo or lottery machine tumbling numbered balls, representing randomised selection.
Conceptual illustration of two puppets dancing, with a large, hidden hand holding the strings for both from above, repre…
Real-life classroom example illustration showing a group of students holding a 'Day 1' calendar sign, representing a bas…
Infographic comparing a tiny handful of sand on the left to a large, overflowing bucket of sand on the right, representi…
Visual of an archery bullseye. An arrow is near the center, surrounded by a highlighted outer circle indicating the acce…
Illustration of an old analog radio tuner dial moving past jagged static lines toward a smooth, glowing soundwave, repre…
Humorous educational doodle of an archer confidently shooting an arrow entirely off the target board and hitting a tree,…
Faded skill examples identifying confounding variables and sampling errors.
Let's practice spotting research flaws by evaluating two reasoning scenarios. In Study A, researchers claim that drinking five cups of coffee daily causes lung cancer, but they failed to check if the participants also smoked cigarettes. In this case, smoking acts as a because it is an unmeasured third factor that influences the outcome, making the cause-and-effect claim invalid. In Study B, a student surveys just ten close friends in a school of two thousand students, concluding that 90% of the school wants online classes. Relying on such a tiny number of participants results in a high , meaning the results cannot reliably represent the entire student body. Furthermore, selecting only close friends instead of a random group introduces a severe , as friends often share similar views and do not reflect the diversity of the school. By systematically identifying these specific flaws, you can accurately evaluate the validity of any scientific or social research claim.
Fill in the blanks with sampling and bias terms.
To ensure all socioeconomic groups were proportionately represented in the sociology project, the students used a sampling method. In the subsequent biology experiment, the trials for the new plant nutrient were completely to prevent any selection bias. Before administering the experimental treatment, the researchers carefully measured the health metrics of the to establish an accurate point of comparison. Later, when analyzing the survey data for their economics paper, the team reported a of plus or minus three percent to account for statistical uncertainty. Finally, during the analysis of the physics lab data, the software applied advanced to remove background interference and isolate the true signal.
Identify methodological concerns in a small-sample study.
Evaluate the reliability of the startup's claim by breaking down their research design.
How does the number of participants impact the margin of error?
Consider if the sample is stratified or randomised.
What else could be causing high scores in this specific baseline cohort besides the app?