1.4.5 - Blinding | STAT 200

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1.4.5 - Blinding

Blinding techniques are also used to avoid bias. In a single-blind study the participants do not know what treatment groups they are in, but the researchers interacting with them do know. In a double-blind study, the participants do not know what treatment groups they are in and neither do the researchers who are interacting with them directly. Double-blind studies are used to prevent researcher bias.

Blinding Procedure employed in research to prevent bias in which the participants and/or the researchers interacting with the participations do not know which treatment each case is receiving Single-Blind Study Research study in which the participants do not know the treatment group that they have been assigned to Double-Blind Study Research study in which neither the participants nor the researchers interacting with them know which cases have been assigned to which treatment groups

Example: Yogurt Tasting Section

Researchers are comparing a low-fat blueberry yogurt to a high-fat blueberry yogurt. Participants are randomly assigned to receive one type of yogurt. After tasting it, they complete an online survey. The researchers know which yogurt containers are low-fat and which are high-fat, but participants are not told. This is an example of a single-blind study because the researchers know which participants are in the low- and high-fat groups but the participants do not know. A double-blind study may not be necessary in this case since the researchers have only minimal contact with the participants.

Example: Caffeine Energy Study Section

Researchers want to know if adult males who consume high amounts of caffeine interact more energetically. They obtain a representative sample and randomly assign half of the participants to take a caffeine pill and half to take a placebo pill. The pills are randomly numbered and coded so at the time the researchers do not know which participants have been given caffeine and which have been given the placebo. All participants are told that they may have been given a caffeine pill. After taking the pill, researchers observe the participants interacting with one another and rate the interactions in terms of level of energy.

This is a double-blind study because neither the researchers nor the participants know who is in which group at the time the data are collected. After the data are collected, researchers can look at the pill codes to determine which groups the participants were in to conduct their analyses. A double-blind study is necessary here because the researchers are observing and rating the participants. If the researchers know who is in the caffeine group they may be more likely to rate their levels of energy as very high because that is consistent with their hypothesis.

• « Previous1.4.4 - Control and Placebo Groups
• Next1.5 - Lesson 1 Summary »

Lessons

• Welcome to STAT 200!

• 0: Prerequisite Skills
  • 0.1 - Review of Algebra
    • 0.1.1 - Order of Operations
    • 0.1.2 - Summations
    • 0.1.3 - Basic Linear Equations
  • 0.2 - Introduction to Minitab
  • 0.3 - Word's Equation Editor
  • 0.4 - Canvas' Equation Editor

• 1: Collecting Data
  • 1.1 - Cases & Variables
    • 1.1.1 - Categorical & Quantitative Variables
    • 1.1.2 - Explanatory & Response Variables
  • 1.2 - Samples & Populations
    • 1.2.1 - Sampling Bias
    • 1.2.2 - Sampling Methods
      • 1.2.2.1 - Minitab: Simple Random Sampling
  • 1.3 - Other Sources of Bias
  • 1.4 - Research Study Design
    • 1.4.1 - Confounding Variables
    • 1.4.2 - Causal Conclusions
    • 1.4.3 - Independent and Paired Samples
    • 1.4.4 - Control and Placebo Groups
    • 1.4.5 - Blinding
  • 1.5 - Lesson 1 Summary

• 2: Describing Data, Part 1
  • 2.1 - Categorical Variables
    • 2.1.1 - One Categorical Variable
      • 2.1.1.1 - Risk and Odds
      • 2.1.1.2 - Visual Representations
        • 2.1.1.2.1 - Minitab: Frequency Tables
        • 2.1.1.2.2 - Minitab: Pie Charts
        • 2.1.1.2.3 - Minitab: Bar Charts
    • 2.1.2 - Two Categorical Variables
      • 2.1.2.1 - Minitab: Two-Way Contingency Table
      • 2.1.2.2 - Minitab: Clustered Bar Chart
      • 2.1.2.3 - Minitab: Stacked Bar Chart
    • 2.1.3 - Probability Rules
      • 2.1.3.1 - Range of Probabilities
      • 2.1.3.2 - Combinations of Events
        • 2.1.3.2.1 - Disjoint & Independent Events
        • 2.1.3.2.2 - Intersections
        • 2.1.3.2.3 - Unions
        • 2.1.3.2.4 - Complements
        • 2.1.3.2.5 - Conditional Probability
          • 2.1.3.2.5.1 - Advanced Conditional Probability Applications
  • 2.2 - One Quantitative Variable
    • 2.2.1 - Graphs: Dotplots and Histograms
    • 2.2.2 - Outliers
    • 2.2.3 - Shape
    • 2.2.4 - Measures of Central Tendency
      • 2.2.4.1 - Skewness & Central Tendency
    • 2.2.5 - Measures of Spread
    • 2.2.6 - Minitab: Central Tendency & Variability
    • 2.2.7 - The Empirical Rule
    • 2.2.8 - z-scores
    • 2.2.9 - Percentiles
    • 2.2.10 - Five Number Summary
  • 2.3 - Lesson 2 Summary

• 3: Describing Data, Part 2
  • 3.1 - Single Boxplot
  • 3.2 - Identifying Outliers: IQR Method
  • 3.3 - One Quantitative and One Categorical Variable
  • 3.4 - Two Quantitative Variables
    • 3.4.1 - Scatterplots
      • 3.4.1.1 - Minitab: Simple Scatterplot
    • 3.4.2 - Correlation
      • 3.4.2.1 - Formulas for Computing Pearson's r
      • 3.4.2.2 - Example of Computing r by Hand (Optional)
      • 3.4.2.3 - Minitab: Compute Pearson's r
    • 3.4.3 - Simple Linear Regression
      • 3.4.3.1 - Minitab: SLR
      • 3.4.3.2 - Example: Interpreting Output
  • 3.5 - Relations between Multiple Variables
    • 3.5.1 - Scatterplot with Groups
    • 3.5.2 - Bubble Plots
    • 3.5.3 - Time Series Plot
  • 3.6 - Lesson 3 Summary

• 4: Confidence Intervals
  • 4.1 - Sampling Distributions
    • 4.1.1 - StatKey Examples
      • 4.1.1.1 - NFL Salaries (One Mean)
      • 4.1.1.2 - Coin Flipping (One Proportion)
    • 4.1.2 - Copying Data into StatKey
    • 4.1.3 - Impact of Sample Size
  • 4.2 - Introduction to Confidence Intervals
    • 4.2.1 - Interpreting Confidence Intervals
    • 4.2.2 - Applying Confidence Intervals
  • 4.3 - Introduction to Bootstrapping
    • 4.3.1 - Example: Bootstrap Distribution for Proportion of Peanuts
    • 4.3.2 - Example: Bootstrap Distribution for Difference in Mean Exercise
  • 4.4 - Bootstrap Confidence Interval
    • 4.4.1 - StatKey: Standard Error Method
      • 4.4.1.1 - Example: Proportion of Lactose Intolerant German Adults
      • 4.4.1.2 - Example: Difference in Mean Commute Times
    • 4.4.2 - StatKey: Percentile Method
      • 4.4.2.1 - Example: Correlation Between Quiz & Exam Scores
      • 4.4.2.2 - Example: Difference in Dieting by Biological Sex
      • 4.4.2.3 - Example: One sample mean sodium content
  • 4.5 - Paired Samples
  • 4.6 - Impact of Sample Size on Confidence Intervals
  • 4.7 - Lesson 4 Summary

• 5: Hypothesis Testing, Part 1
  • 5.1 - Introduction to Hypothesis Testing
  • 5.2 - Writing Hypotheses
    • 5.2.1 - Examples
  • 5.3 - Randomization Procedures
    • 5.3.1 - StatKey Randomization Methods (Optional)
  • 5.4 - p-values
  • 5.5 - Randomization Test Examples in StatKey
    • 5.5.1 - Single Proportion Example: PA Residency
    • 5.5.2 - Paired Means Example: Age
    • 5.5.3 - Difference in Means Example: Exercise by Biological Sex
    • 5.5.4 - Correlation Example: Quiz & Exam Scores
  • 5.6 - Lesson 5 Summary

• 6: Hypothesis Testing, Part 2
  • 6.1 - Type I and Type II Errors
  • 6.2 - Significance Levels
  • 6.3 - Issues with Multiple Testing
  • 6.4 - Practical Significance
  • 6.5 - Power
  • 6.6 - Confidence Intervals & Hypothesis Testing
  • 6.7 - Lesson 6 Summary

• 7: Normal Distributions
  • 7.1 - Standard Normal Distribution
  • 7.2 - Minitab: Finding Proportions Under a Normal Distribution
    • 7.2.1 - Proportion 'Less Than'
      • 7.2.1.1 - Example: P(Z<-1)
      • 7.2.1.2 - Example: P(SATM<540)
    • 7.2.2 - Proportion 'Greater Than'
      • 7.2.2.1 - Example: P(Z>0.5)
    • 7.2.3 - Proportion 'In between'
      • 7.2.3.1 - Example: Proportion Between z -2 and +2
    • 7.2.4 - Proportion 'More Extreme Than'
  • 7.3 - Minitab: Finding Values Given Proportions
    • 7.3.1 - Top X%
    • 7.3.2 - Bottom X%
    • 7.3.3 - Middle X%
  • 7.4 - Central Limit Theorem
    • 7.4.1 - Hypothesis Testing
      • 7.4.1.1 - Video Example: Mean Body Temperature
      • 7.4.1.2 - Video Example: Correlation Between Printer Price and PPM
      • 7.4.1.3 - Example: Proportion NFL Coin Toss Wins
      • 7.4.1.4 - Example: Proportion of Women Students
      • 7.4.1.5 - Example: Mean Quiz Score
      • 7.4.1.6 - Example: Difference in Mean Commute Times
    • 7.4.2 - Confidence Intervals
      • 7.4.2.1 - Video Example: 98% CI for Mean Atlanta Commute Time
      • 7.4.2.2 - Video Example: 90% CI for the Correlation between Height and Weight
      • 7.4.2.3 - Example: 99% CI for Proportion of Women Students
  • 7.5 - Lesson 7 Summary

• 8: Inference for One Sample
  • 8.1 - One Sample Proportion
    • 8.1.1 - Confidence Intervals
      • 8.1.1.1 - Normal Approximation Formulas
        • 8.1.1.1.1 - Video Example: PA Residency
        • 8.1.1.1.2 - Video Example: Dog Ownership
        • 8.1.1.1.3 - Video Example: Books
        • 8.1.1.1.4 - Example: Retirement
      • 8.1.1.2 - Minitab: Confidence Interval for a Proportion
        • 8.1.1.2.1 - Example with Summarized Data
        • 8.1.1.2.2 - Example with Summarized Data
      • 8.1.1.3 - Computing Necessary Sample Size
    • 8.1.2 - Hypothesis Testing
      • 8.1.2.1 - Normal Approximation Method Formulas
        • 8.1.2.1.1 - Video Example: Male Babies
        • 8.1.2.1.2 - Example: Handedness
        • 8.1.2.1.3 - Example: Ice Cream
        • 8.1.2.1.4 - Example: Overweight Citizens
      • 8.1.2.2 - Minitab: Hypothesis Tests for One Proportion
        • 8.1.2.2.1 - Minitab: 1 Proportion z Test, Raw Data
        • 8.1.2.2.2 - Minitab: 1 Sample Proportion z test, Summary Data
          • 8.1.2.2.2.1 - Minitab Example: Normal Approx. Method
  • 8.2 - One Sample Mean
    • 8.2.1 - t Distribution
    • 8.2.2 - Confidence Intervals
      • 8.2.2.1 - Formulas
        • 8.2.2.1.1 - Example: MLB Age
        • 8.2.2.1.2- Example: Sleep Deprivation
        • 8.2.2.1.3 - Example: Milk
      • 8.2.2.2 - Minitab: Confidence Interval of a Mean
        • 8.2.2.2.1 - Example: Age of Pitchers (Summarized Data)
        • 8.2.2.2.2 - Example: Coffee Sales (Data in Column)
      • 8.2.2.3 - Computing Necessary Sample Size
        • 8.2.2.3.1 - Example: Estimating IQ
        • 8.2.2.3.2 - Video Example: Age
        • 8.2.2.3.3 - Video Example: Cookie Weights
    • 8.2.3 - Hypothesis Testing
      • 8.2.3.1 - One Sample Mean t Test, Formulas
        • 8.2.3.1.1 - Video Example: Book Costs
        • 8.2.3.1.2 : Example: Pulse Rate
        • 8.2.3.1.3 - Example: Coffee
        • 8.2.3.1.4 - Example: Transportation Costs
      • 8.2.3.2 - Minitab: One Sample Mean t Tests
        • 8.2.3.2.1 - Minitab: 1 Sample Mean t Test, Raw Data
        • 8.2.3.2.2 - Minitab: 1 Sample Mean t Test, Summarized Data
      • 8.2.3.3 - One Sample Mean z Test (Optional)
  • 8.3 - Paired Means
    • 8.3.1 - Confidence Intervals
      • 8.3.1.1. - Example: Change in Knowledge
      • 8.3.1.2 - Video Example: Difference in Exam Scores
    • 8.3.2 - Hypothesis Testing
      • 8.3.2.1 - Example: Quiz Scores
    • 8.3.3 - Minitab: Paired Means Test
      • 8.3.3.1 - Example: SAT Scores
      • 8.3.3.2 - Example: Marriage Age (Summarized Data)
  • 8.4 - Lesson 8 Summary

• 9: Inference for Two Samples
  • 9.1 - Two Independent Proportions
    • 9.1.1 - Confidence Intervals
      • 9.1.1.1 - Minitab: Confidence Interval for 2 Proportions
    • 9.1.2 - Hypothesis Testing
      • 9.1.2.1 - Normal Approximation Method Formulas
        • 9.1.2.1.1 – Example: Ice Cream
        • 9.1.2.1.2 – Example: Same Sex Marriage
      • 9.1.2.2 - Minitab: Difference Between 2 Independent Proportions
        • 9.1.2.2.1 - Example: Dating
  • 9.2 - Two Independent Means
    • 9.2.1 - Confidence Intervals
      • 9.2.1.1 - Minitab: Confidence Interval Between 2 Independent Means
        • 9.2.1.1.1 - Video Example: Mean Difference in Exam Scores, Summarized Data
    • 9.2.2 - Hypothesis Testing
      • 9.2.2.1 - Minitab: Independent Means t Test
        • 9.2.2.1.1 - Example: Summarized Data
        • 9.2.2.1.3 - Example: Height by Sex
  • 9.3 - Lesson 9 Summary

• 10: One-Way ANOVA
  • 10.1 - Introduction to the F Distribution
  • 10.2 - Hypothesis Testing
  • 10.3 - Pairwise Comparisons
  • 10.4 - Minitab: One-Way ANOVA
  • 10.5 - Example: SAT-Math Scores by Award Preference
  • 10.6 - Example: Exam Grade by Professor
  • 10.7 - Lesson 10 Summary

• 11: Chi-Square Tests
  • 11.1 - Reviews
    • 11.1.1 - Frequency Table
    • 11.1.2 - Two-Way Contingency Table
    • 11.1.3 - Probability Distribution Plots
    • 11.1.4 - Conditional Probabilities and Independence
  • 11.2 - Goodness of Fit Test
    • 11.2.1 - Five Step Hypothesis Testing Procedure
      • 11.2.1.1 - Video: Cupcakes (Equal Proportions)
      • 11.2.1.2- Cards (Equal Proportions)
      • 11.2.1.3 - Roulette Wheel (Different Proportions)
    • 11.2.2 - Minitab: Goodness-of-Fit Test
      • 11.2.2.1 - Example: Summarized Data, Equal Proportions
      • 11.2.2.2 - Example: Summarized Data, Different Proportions
  • 11.3 - Chi-Square Test of Independence
    • 11.3.1 - Example: Gender and Online Learning
    • 11.3.2 - Minitab: Test of Independence
      • 11.3.2.1 - Example: Raw Data
      • 11.3.2.2 - Example: Summarized Data
    • 11.3.3 - Relative Risk
  • 11.4 - Lesson 11 Summary

• 12: Correlation & Simple Linear Regression
  • 12.1 - Review: Scatterplots
  • 12.2 - Correlation
    • 12.2.1 - Hypothesis Testing
      • 12.2.1.1 - Example: Quiz & Exam Scores
      • 12.2.1.2 - Example: Age & Height
      • 12.2.1.3 - Example: Temperature & Coffee Sales
    • 12.2.2 - Correlation Matrix
      • 12.2.2.1 - Example: Student Survey
      • 12.2.2.2 - Example: Body Correlation Matrix
  • 12.3 - Simple Linear Regression
    • 12.3.1 - Formulas
    • 12.3.2 - Assumptions
    • 12.3.3 - Minitab - Simple Linear Regression
    • 12.3.4 - Hypothesis Testing for Slope
      • 12.3.4.1 - Example: Quiz and exam scores
      • 12.3.4.2 - Example: Business Decisions
    • 12.3.5 - Confidence Interval for Slope
      • 12.3.5.1 - Example: Quiz and exam scores
  • 12.4 - Coefficient of Determination
  • 12.5 - Cautions
  • 12.6 - Correlation & Regression Example
  • 12.7 - Lesson 12 Summary

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