Percent Increase and Decrease Quiz
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Programs & methods
Psychology Lab Weber's Law and Hearing: How Much Louder to Notice a Difference?
Students apply Weber's Law (ΔI = k × I, where k = 0.1 for hearing) to calculate the just noticeable difference (JND) in loudness for ten stimulus intensities ranging from 20 to 110 dB. After completing the data table, students plot intensity on the x-axis and JND on the y-axis, identify the linear relationship, and explain in everyday language how the JND grows proportionally with intensity. A real-world application problem asks students to calculate the minimum loudness increase a concert-goer
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Weber's Law and Smell: How Much Stronger Before You Notice?
Students apply Weber's Law (ΔI = k × I, where k = 0.05 for smell) to calculate JNDs for ten odor concentrations ranging from 20 to 500 AU (arbitrary units). After completing the data table and plotting the graph, students describe the linear intensity-JND relationship and explain how the olfactory system's sensitivity relates proportionally to baseline concentration. A real-world problem asks students to calculate the minimum odor increase a fragrance evaluator named Parfumia would detect at 120
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Weber's Law and Taste: How Much Saltier Before You Notice?
Students apply Weber's Law (ΔI = k × I, where k = 0.20 for saltiness) to calculate JNDs for ten salt concentrations ranging from 1 to 40 g/L. The relatively high k value for taste (compared to vision and kinesthesis) gives students data that demonstrates taste's lower sensitivity to proportional changes. After graphing, students describe the linear relationship and explain what the larger k value implies about gustatory discrimination. A real-world problem asks students to calculate the minimum
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Weber's Law and Temperature: How Hot Before You Feel the Diff.
Students apply Weber's Law (ΔI = k × I, where k = 0.07 for temperature) to calculate JNDs for ten temperatures ranging from 10 to 55°C. The lab introduces students to thermal sensation as a measurable, Weber's Law-governed sensory modality. After graphing and identifying the linear relationship, students explain how the JND for temperature grows with baseline temperature. A real-world problem asks students to calculate the minimum temperature increase a hot tub enthusiast named Chilldaddy would
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Weber's Law and Touch: How Much Pressure Before You Feel More?
Students apply Weber's Law (ΔI = k × I, where k = 0.14 for touch/pressure) to calculate JNDs for ten pressure intensities ranging from 50 to 1500 g. The wide range of values — from light touch to heavy pressure — gives students data that clearly illustrates the proportional scaling of JND across a broad sensory range. After graphing, students describe the linear relationship. A real-world problem asks students to calculate the minimum pressure increase a massage therapist named Squishy would nee
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Weber's Law and Vision: How Much Brighter Before Your Eye Notices
Students apply Weber's Law (ΔI = k × I, where k = 0.02 for brightness) to calculate JNDs for ten light intensities ranging from 100 to 3000 candelas (cd). The very low k value for vision — the lowest in the collection alongside kinesthesis — demonstrates that the visual system is among the most sensitive to proportional changes, requiring only a 2% change for detection. After graphing, students note the linear relationship and discuss the implications of the small k value. A real-world problem a
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Weber's Law and Kinesthesis: How Much Heavier Before You Feel It?
Students apply Weber's Law (ΔI = k × I, where k = 0.02 for kinesthesis) to calculate JNDs for ten lifted weights ranging from 100 to 5000 g. Sharing the same k value as vision (0.02), this lab allows for cross-modal comparison of sensitivity and demonstrates that the kinesthetic system — despite sensing a very different type of stimulus — matches visual sensitivity in proportional discrimination. After graphing, students describe the linear relationship. A real-world problem asks students to cal
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
AP Macroeconomics | No Prep FRQs | Opportunity Cost & Comparative Advantage | |
FRQ 1: Basic Economic Concepts – Opportunity Cost & Comparative AdvantageTitle:AP Macroeconomics | Opportunity Cost & Comparative Advantage | No-Prep FRQ | Description: Overview: This FRQ challenges students to analyze how opportunity cost and comparative advantage shape international trade. Using Adam Smith’s original quote and real-world trade examples, learners apply theory to data-driven decisions about specialization and efficiency. How It Can Be Used: 🌎 As a class discussion starter on
9th - 12th, Adult Education, Higher Education
Economics , Other (Social Studies), Statistics
Also included in: AP Macroeconomics | No Prep FRQs Bundle | 10 Complete Prompts |
T-Test & Hypothesis Testing Assignment | Find P-Value & Drawing Conclusions
Reinforce key statistics concepts with this no-prep T-Test & Hypothesis Testing Assignment, designed for IB Math, AP Statistics, or any high school/college stats course. This assignment gives students hands-on practice with two-sample t-tests, guiding them through real-world data analysis and statistical reasoning. What’s Included: Practice identifying null and alternative hypotheses Interpreting the p-value to assess significance Performing and analyzing two-sample t-tests Drawing c
10th - 12th, Higher Education
Other (Math), Statistics
CCSS
HSS-IC.B.4
AP Statistics Applying the Normal Distribution Practice
This activity includes 1 guided practice scenario and 2 practice quizzes aligned to the AP Statistics course requirements for the normal distribution. A detailed answer key is included. Included topics include the Empirical Rule, calculating probabilities, understanding z-scores, and using the inverse norm
11th - 12th, Higher Education
Statistics
The Geometric Distribution (4.12 Mega Practice Set)
This Mega Practice Set of 17 pages provides a complete and in-depth collection of 50 original, AP-style multiple-choice questions covering Geometric Distributions, fully aligned with AP Statistics Unit 4.12. The questions are carefully structured into five levels of increasing depth, guiding students from basic probability calculations to advanced interpretation and parameter reasoning—exactly the skills required for AP Statistics success. Perfect for classroom instruction, homework, assess
10th - 12th, Higher Education
Statistics
Also included in: The Geometric Distribution (4.12 AP Statistics Theory and Practice)
Psychology Lab Investigative Career Interests: Do Juniors or Seniors Score Highe
Students complete the IIP RIASEC Markers Holland Code assessment at openpsychometrics.org and record only their Investigative (I) score, which reflects interest in science, research, and analytical thinking. Students pool Investigative scores with classmates, separating results by grade level. Junior and senior scores are entered into an independent samples t-test to evaluate whether career interest differences are statistically significant. Students analyze group averages, interpret statistical
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Juniors vs. Seniors: Who Is More Hypersensitive?
Students complete the Hypersensitive Narcissism Scale (HSNS) measuring covert narcissism — characterized by hypersensitivity to criticism, self-absorption, and fragile self-esteem — then collect scores from junior and senior classmates to run an independent samples t-test. The lab is one of the more conceptually sophisticated in the collection, asking students to consider whether hypersensitive narcissism might change across high school years. Juniors navigating high-stakes performance pressure
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Autism Spectrum Traits in Juniors vs. Seniors: A t-Test Lab
Students complete the Autism Spectrum Quotient (AQ), a widely used self-report measure of autism-spectrum-associated traits such as social skill differences, attention switching, and attention to detail. Students record their scores and contribute to a class dataset organized by grade level. Junior and senior scores are entered into an independent samples t-test to determine whether differences in autism spectrum trait expression are statistically significant across grade levels. Students interp
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Tired and Grumpy: Does Sleep Quality Predict Negative Affect?
Tired and Grumpy: Does Sleep Quality Predict Negative Affect? Students complete the Groningen Sleep Quality Scale (GSQS) and the PANAS Negative Affect subscale, then pool data to calculate a Pearson r. The lab connects sleep neuroscience — specifically amygdala reactivity to sleep deprivation — to emotional experience, providing a biological mechanism for the predicted correlation. Students analyze the bidirectional cycle in which poor sleep increases negative emotion and negative emotion disr
8th - 12th, Adult Education, Higher Education
Psychology, Statistics
Psychology Lab Juniors vs. Seniors: Do Femininity Traits Change? A BSRI Lab
Students complete the Bem Sex Role Inventory (BSRI) and record only their Femininity subscale score, then collect scores from junior and senior classmates to run an independent samples t-test. The Femininity subscale measures self-reported traits historically associated with femininity — warmth, nurturance, sensitivity, and compassion — regardless of the students gender. The lab examines whether these interpersonally oriented traits change meaningfully between junior and senior year, and invites
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Want Friends, Fear People: Unmet Belonging Needs and Social Anx.
Students complete the Belonging/Love subscale of a Maslow-based needs assessment and the Liebowitz Social Anxiety Scale (LSAS), then pool paired scores from 9 classmates to calculate a Pearson r. The lab examines a clinically important paradox: people who most want social connection may simultaneously be most afraid of it. Students explain the psychological mechanism — unmet belonging needs can intensify the stakes of social evaluation, increasing fear of rejection and feeding social anxiety — a
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Is It Anxiety or Just About Health? Comparing Two Constructs
Is It Anxiety or Just About Health? Comparing Two Anxiety Constructs Students complete the Short Health Anxiety Inventory (HAI-18) and the GAD-7 General Anxiety scale, then pool data to calculate a Pearson r. The lab examines whether health anxiety is a specific form of general anxiety or a distinct clinical construct, and explores how heightened attention to bodily sensations differs from generalized worry. Discussion connects the lab to post-pandemic increases in health anxiety and challenge
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Psychology Lab Juniors vs. Seniors: Who Feels More Connected to Nature?
Students complete the Connectedness to Nature Scale (CNS), which measures the degree to which a person feels part of the natural world, then collect scores from junior and senior classmates to run an independent samples t-test. The lab explores whether environmental identity and nature connectedness — shown in research to buffer stress and support well-being — differs between the two grade levels. Students consider whether time spent outdoors, exposure to nature during adolescence, or the increa
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Probability Trees No-Prep Mini Unit Bundle | Notes, Activity, and Assignment
Make teaching probability trees simple, interactive, and effective with this all-in-one mini unit bundle! Designed for IB Math, Statistics, or any high school probability unit, this no-prep resource includes guided notes, a creative life-based project, and an additional assignment for practice and assessment. Whether you're introducing tree diagrams or reviewing compound probability, this bundle has everything you need to help students understand and apply probability in real-world and imagin
10th - 12th, Higher Education
Algebra 2, Other (Math), Statistics
CCSS
HSS-CP.A.1
, HSS-CP.A.2
, HSS-CP.A.3
+4
Psychology Lab Put Down the Phone and Succeed? Internet Use vs Self-Efficacy
Put Down the Phone and Succeed? Internet Use and Academic Self-Efficacy Students complete the Internet Addiction Assessment (IAA) and the Generalized Self-Efficacy Scale (GSE), then pool data to calculate a Pearson r between problematic internet use and academic self-efficacy. The lab connects variable ratio reinforcement from social media and gaming to the difficulty of controlling internet behavior, and explores whether low academic self-efficacy might cause escape into internet use. Student
8th - 12th, Adult Education, Higher Education
Health, Psychology, Statistics
Two-Sample Proportion Inference (AP Statistics 6.10)
Confidently conduct and understand two-sample hypothesis tests for population proportions with these structured, exam-ready Mega Smart Notes, fully aligned with AP Statistics Unit 6.10. This resource focuses on correct procedure selection, pooled proportion logic, required conditions, and proper hypothesis setup—key elements tested on AP FRQs and MCQs.
10th - 12th, Higher Education
Math Test Prep, Statistics
Also included in: AP Statistics Unit 6: Inference for proportions
Probability Normal Distribution FRQ Practice (AP Statistics Style)
Get your students thinking, reasoning, and applying key AP Statistics concepts with this engaging real-world Free Response Question (FRQ)! In The University Coffee Shop, students dive into a relatable scenario involving coffee choices, probability rules, and normal distributions — all while practicing the exact skills needed for AP exam success. This resource brings rigor and relevance together: perfect for classwork, review, or test prep!💡 What’s Inside:✅ A full AP-style FRQ (multi-part,
10th - 12th, Higher Education
Statistics
Chi-Square Tests | No-Prep Guided Notes for IB Math or Stats Class
Help your students master Chi-Square Tests with these clear, structured, and no-prep guided notes, perfect for IB Math Applications and Interpretations, AP Statistics, or any upper-level statistics course. These notes break down complex concepts into approachable steps with definitions, examples, and exam-style practice. ✅ What’s Included: Step-by-step instruction on: Chi-Square Tests for Frequency Tables Degrees of Freedom Expected Frequencies Goodness of Fit Tests Guided examples
10th - 12th, Higher Education
Other (Math), Statistics
CCSS
HSS-IC.A.2
, HSS-IC.B.4
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