Activity 2.1.2 Student Response Sheet Answers

Activity 2.1.2 Student Response Sheet Answers

Activity 2.1.2 focuses on analyzing statistical data and identifying biases in research studies. It provides examples of how misleading averages can skew public perception, particularly in health and safety statistics. This response sheet is designed for students studying biomedical innovation, helping them understand the importance of accurate data representation. Key topics include sample size, study design flaws, and the manipulation of survey questions. Ideal for students preparing for assessments in biomedical science or related fields.

Key Points

  • Analyzes statistical data and identifies biases in research studies.
  • Explains the impact of sample size on the accuracy of study results.
  • Discusses how study design flaws can lead to misleading conclusions.
  • Highlights the importance of accurate data representation in health statistics.
  • newtopiccyclegrowin
403
/ 5
Activity 2.1.2 Student Response Sheet
Part I: Answers
Example 1:
Prosthetic, Inc. and Orthotics and Prosthetics had almost identical profit increases
between the years 1998 and 2009. The y-axis on Orthotics and Prosthetics’ graph
has been altered to have a larger range than the range used for the y-axis on
Prosthetic, Inc.’s graph. If the same range was used for both graphs, they would look
as follows:
Example 2:
ABC.com presented that the average blood sugar level before meals for Americans
is 142 mg/dL but did not specify how the average was calculated. What they did not
share was that the mean, or arithmetic average, was calculated from a sample size
of only four people. Of these four people, all of the blood sugar levels (before meals)
were in normal ranges and one person had a blood sugar level of 240 mg/dL,
causing the entire group’s average to be skewed. Therefore, 75% of the blood sugar
levels of the people in the study were actually far below this average. A better
average to use would have been the median blood sugar level (i.e., the blood sugar
level in the middle of the data range). The website should have reported how many
people were included in the study. Four people do not accurately represent the
entire population.
Example 3:
The data presented was biased as it did not present all of the pertinent information.
The number of motorcycles on the road is significantly less than the number of
passenger cars on the road, so simply using the number of vehicles involved in fatal
crashes is not enough to illustrate which vehicle is the safest. The following two
graphs, which show the rate of vehicles involved in fatal crashes per 100 million
miles travelled and the rate of vehicles involved in fatal crashes per 100,000
registered vehicles, represent the data more accurately. Using all of the data as a
whole, passenger cars are actually involved in fewer fatal crashes than the other
vehicles.
© 2011 Project Lead The Way, Inc.
Biomedical Innovation Activity 2.1.2 Student Response Sheet – Page 1
© 2011 Project Lead The Way, Inc.
Biomedical Innovation Activity 2.1.2 Student Response Sheet – Page 2
Part II: Ways in Which Statistics Can Be Misused
Biased Study Design
Note: A sample is defined as a representative part of a population whose properties are
studied to gain information about the whole.
1. Sample is not representative of the entire population being studied.
Example _____
Example _____
2. Sample size is too small to accurately represent the entire population being
studied.
Example _____
3. Study design is flawed.
Example _____
4. Survey questions can be manipulated by the researcher so that the
participants are more likely to answer in a specific way.
Example _____
Data Manipulation
1. Researcher over-generalizes conclusions drawn from study results.
Example _____
2. Unfavorable data is not presented.
Example _____
3. Values, such as percentages, are used to present data without indicating
what the numbers actually mean.
Example _____
4. Causation is implied from the results of a study that only found a correlation
between two variables.
Example _____
5. Averages are often used to present the “normal values” of a set of data, but
they use an average that is inappropriate for the data set and therefore
misrepresent the data. The word “average” has a loose meaning. Mean,
median, and mode are all types of averages, but all have different meanings
© 2011 Project Lead The Way, Inc.
Biomedical Innovation Activity 2.1.2 Student Response Sheet – Page 3
/ 5
End of Document
403
You May Also Like

FAQs of Activity 2.1.2 Student Response Sheet Answers

What are common ways statistics can be misused in research?
Statistics can be misused through biased study designs, such as using non-representative samples or small sample sizes that do not accurately reflect the population. Additionally, researchers may manipulate survey questions to elicit specific responses, leading to skewed data. Unfavorable data might be omitted from reports, and averages can be presented without context, making them misleading. Understanding these pitfalls is crucial for interpreting research findings accurately.
How does sample size affect the validity of a study?
Sample size is critical in research as a small sample may not represent the larger population, leading to inaccurate conclusions. For example, if a study on blood sugar levels only includes a few individuals, the average may be skewed by outliers, misrepresenting the true population's health. A larger, more representative sample provides a clearer picture and increases the reliability of the results, making it essential for robust scientific research.
What is the difference between mean and median in data analysis?
The mean is the arithmetic average of a data set, calculated by summing all values and dividing by the number of observations. However, the mean can be heavily influenced by extreme values, making it less reliable in skewed distributions. The median, on the other hand, represents the middle value when data is sorted, providing a better measure of central tendency in such cases. Understanding when to use mean versus median is vital for accurate data interpretation.
What are the implications of biased study designs?
Biased study designs can lead to misleading conclusions that affect public policy and health recommendations. For instance, if a study on vehicle safety only includes data from a small number of motorcycle accidents, it may falsely suggest that motorcycles are more dangerous than they are compared to cars. Such biases can misinform stakeholders and the public, emphasizing the need for rigorous study designs that accurately reflect the population being studied.

Related of Activity 2.1.2 Student Response Sheet Answers