/*! This file is auto-generated */ .wp-block-button__link{color:#fff;background-color:#32373c;border-radius:9999px;box-shadow:none;text-decoration:none;padding:calc(.667em + 2px) calc(1.333em + 2px);font-size:1.125em}.wp-block-file__button{background:#32373c;color:#fff;text-decoration:none} Problem 98 Select the best answer Two esse... [FREE SOLUTION] | 91影视

91影视

Log out

Learning Materials

Features

Discover

Chapter 4: Problem 98

Select the best answer Two essential features of all statistically designed experiments are (a) compare several treatments; use the double-blind method. (b) compare several treatments; use chance to assign subjects to treatments. (c) always have a placebo group; use the double-blind method. (d) use a block design; use chance to assign subjects to treatments. (e) use enough subjects; always have a control group.

Short Answer

Expert verified
Option (b) is the best answer: compare several treatments and use chance to assign subjects to treatments.

Step by step solution

01

Understand Experimental Design

Statistically designed experiments aim to make valid inferences about the effects of different treatments. Two critical features are needed: comparing treatments to understand their effectiveness and using randomness to reduce bias.
02

Compare Treatments

In any well-designed experiment, comparing different treatments is necessary to determine which has the most significant impact. Without comparison, we can鈥檛 evaluate the relative effectiveness of the treatments.
03

Use of Random Assignment

To ensure that the results are not biased, subjects or experimental units should be randomly assigned to different treatments. This randomization helps in creating groups that are comparable before applying the treatments and controls for confounding variables.
04

Evaluate the Options

Option (a) mentions comparing treatments and the double-blind method, but the double-blind method is not essential for all experiments. Option (b) includes comparing treatments and using chance to assign subjects, both of which are fundamental. Options (c), (d), and (e) either include non-essential elements or omit critical features.

Unlock Step-by-Step Solutions & Ace Your Exams!

  • Full Textbook Solutions

    Get detailed explanations and key concepts

  • Unlimited Al creation

    Al flashcards, explanations, exams and more...

  • Ads-free access

    To over 500 millions flashcards

  • Money-back guarantee

    We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91影视!

Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Treatment Comparison
When discussing statistical experiment design, one of the foundational concepts is the comparison of treatments. This approach involves evaluating multiple interventions to understand their effectiveness better. By comparing treatments, researchers can identify which option yields the most significant and beneficial outcomes.

Treatment comparison provides a framework to:
  • Assess varying effectiveness between different interventions.
  • Determine best practices or superior methods.
  • Quantitatively analyze treatment results for informed decisions.
Imagine conducting an experiment without comparing treatments鈥攊t would be challenging to understand which intervention is truly moving the needle. Thus, treatment comparison is indispensable for drawing meaningful conclusions.
Random Assignment
Random assignment plays a crucial role in ensuring the integrity and reliability of experimental results. In a well-designed statistical experiment, subjects or units must be randomly assigned to different treatment groups. This process aims to distribute any unique characteristics evenly among the groups.

Random assignment is essential because it helps:
  • Create comparable groups before treatment application.
  • Avoid systematic differences that could skew results.
  • Increase the study's internal validity by eliminating selection biases.
Through random assignment, each participant has an equal opportunity of being placed in any treatment group. This randomness neutralizes biases, making the results more trustworthy and generalized.
Experimental Bias Reduction
Reducing bias in experiments ensures that the data collected is as accurate and unbiased as possible. Several strategies in experiment design focus on minimizing bias, with randomization being one of the key methodologies. Randomization reduces the chances of predictable patterns that can distort the findings.

Bias reduction helps to:
  • Enhance the credibility and reliability of experimental results.
  • Support a fair representation of treatment effects.
  • Eliminate alternative explanations for observed outcomes.
Other strategies to minimize bias can include blinding the participants and researchers to which treatment is being given. Although double-blind methods are not always essential, they are excellent for avoiding biases associated with participant and researcher expectations.
Control of Confounding Variables
Controlling for confounding variables is another fundamental aspect of statistical experiment design. Confounding variables are extraneous factors that can mislead the apparent relationship between independent and dependent variables. If not properly controlled, they can produce invalid or questionable conclusions.

To manage confounding variables, researchers might:
  • Use random assignment to equally distribute confounders across treatment groups.
  • Implement matching techniques to pair subjects with similar confounding characteristics.
  • Utilize statistical controls to adjust for potential confounders in the analysis phase.
By effectively managing these variables, researchers ensure that the observed effects are due solely to the treatments themselves, allowing for more valid and reliable interpretations of the data.

One App. One Place for Learning.

All the tools & learning materials you need for study success - in one app.

Get started for free

Most popular questions from this chapter

According to Louann Brizendine, author of The Female Brain, women say nearly three times as many words per day as men. Skeptical researchers devised a study to test this claim. They used electronic devices to record the talking patterns of 396 university students who volunteered to participate in the study. The device was programmed to record 30 seconds of sound every 12.5 minutes without the carrier鈥檚 knowledge. According to a published report of the study in Scientific American, 鈥淢en showed a slightly wider variability in words uttered.... But in the end, the sexes came out just about even in the daily averages: women at 16,215 words and men at 15,669.鈥漒(^{56}\) This difference was not statistically significant. What conclusion can we draw from this study? Explain.

A recent online poll posed the question 鈥淪hould female athletes be paid the same as men for the work they do?鈥欌�� In all, 13,147 (44%) said 鈥淵es,鈥欌�� 15,182 (50%) said 鈥淣o,鈥欌�� and the remaining 1448 said 鈥淒on鈥檛 know.鈥� In spite of the large sample size for this survey, we can鈥檛 trust the result. Why not?

A large retailer prepares its customers鈥� monthly credit card bills using an automatic machine that folds the bills, stuffs them into envelopes, and seals the envelopes for mailing. Are the envelopes completely sealed? Inspectors choose 40 envelopes from the 1000 stuffed each hour for visual inspection. Identify the population and the sample.

Do smaller classes in elementary school really benefit students in areas such as scores on standardized tests, staying in school, and going on to college? We might do an observational study that compares students who happened to be in smaller and larger classes in their early school years. Identify a lurking variable that may lead to confounding with the effects of small classes. Explain how confounding might occur.

Some Internet service providers (ISPs) charge companies based on how much bandwidth they use in a month. One method that ISPs use for calculating bandwidth is to find the 95th percentile of a company鈥檚 usage based on samples of hundreds of 5-minute intervals during a month. (a) Explain what 鈥�95th percentile鈥� means in this setting. (b) Which would cost a company more: the 95th percentile method or a similar approach using the 98th percentile? Justify your answer.
See all solutions

Recommended explanations on Math Textbooks

Geometry

Read Explanation

Mechanics Maths

Read Explanation

Discrete Mathematics

Read Explanation

Theoretical and Mathematical Physics

Read Explanation

Decision Maths

Read Explanation

Logic and Functions

Read Explanation
View all explanations

What do you think about this solution?

We value your feedback to improve our textbook solutions.

Study anywhere. Anytime. Across all devices.