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  Studying Data Interpretation
 

In this lesson you answer a research question using WOW data. Your final presentation is a scientific poster. The "Reporting Results" section provides specific instructions for formatting the final presentation.

Knowledge Base
Many student experiments are designed to demonstrate a particular principle discussed in lecture. All of the exercises should work since they have been tested prior to your arrival in the laboratory. This isn't how scientific research is performed.

Consider how scientific research might differ from laboratory exercises.

Researchers begin with observations and questions that lead them to a hypothesis. An experiment is then performed, and the results confirm the hypothesis or provide reason for change. Sometimes experimental results point the researcher in a completely new direction.

Experimental results and the researcher's interpretation of them are crucial to the development of new ideas, hypotheses, explanations, and theories. A researcher's ability to interpret data can be both a learned skill and a gift. Although some individuals are better at data interpretation than others, practice sharpens observation and interpretive skills and leads to a more complete analysis of the data.

Be certain to review data as you prepare to answer your question. Think about relationships among data and relationships among data and the external factors. Also consider your reflection as you form a hypothesis.

Initially, focus on no more than two variables. Record your thoughts, impressions, and ideas on paper. Review these periodically. Think about relationships among data. Think about relationships among the data and other external environmental factors, such as rain, wind, sunlight, etc. Use your reflections to form a hypothesis.

Experimental Design
Your teacher will assign a research question.

Possible Research Questions:

  1. What water depth is most affected by sunlight? Why?
  2. How does thermal stratification change throughout the summer months? Why?
  3. At what depth does biological activity seem to be most significant? Why?
  4. Which lake exhibits greater biological activity? Why?
  5. During what time of the year is biological activity at a maximum? Why?
  6. Are daily pH and dissolved oxygen levels related to one another? Why?
  7. Where in the lake are the largest daily pH swings observed? Why?
  8. What depth exhibits the greatest daily changes in dissolved oxygen? Why?
  9. Is there a relationship between daily water temperatures and dissolved oxygen levels? Why?
  10. How does dissolved oxygen depend on depth? Why?
  11. How does lake water temperature depend on depth? Why?
  12. Can the effects of storm activity be identified within the RUSS data? How?
  13. Is there a relationship between conductivity and pH? Explain.
  14. Are there seasonal patterns in turbidity? Explain.
  15. Does the relationship between a lake's acreage and its watershed's acreage affect turbidity? Explain.
  16. Is there a relationship between cooling days (or degree days) and the onset of stratification in a lake?

Data Collection
Access the WOW site and download the required data in Excel format.

Other resources available include class notes, handouts, books in the library, and the Internet. Additional resources are available on the WOW site. Clearly reference any resources used in the data analysis on the poster.

Data Management and Analysis
Use the RUSS data to answer your research question. Be sure to title your graphs and labels axes and legends.

Remember that all experimental data consist of measurements that have one, rightmost uncertain digit. Although the RUSS unit is a sophisticated robot, it is still only a measurement tool. Be sure to consider how much you believe each of the digits in any measurement.

Sometimes, data are found that defy the observed pattern. These are known as data outliers. Rather than dismiss them as unimportant, try to determine their cause. (e.g.: Is the probe working properly?) Sometimes outliers lead to new and interesting interpretations of the data. Were there any outliers in the data you collected? Be prepared to explain how you chose to handle outliers in your data analysis.

Endless tables of numbers can be difficult to understand. A better method is to present the data in a visual or graphical format (i.e.: Excel). Remember that graphs don't have to display the origin. Often subtle and important variations are only observable if the graph axes are modified to expand the data in question (Figure below).

two graphs showing different origin

Also, take advantage of Excel's multiple graphing ability (Figure below). It can be very useful to display more than one graph at a time in order to determine relationships between sets of data.

graph

Interpretation of Results
Consider the following questions as you plan your poster and final presentation.

  • Was data collected by RUSS possibly affected by external factors?
  • Is there sufficient data to answer the research question ?
  • What is the best way to display the data?
  • Are there additional experiments to conduct?
  • Did you find any outliers? How can the outliers be explained?
  • Are there unanswered questions?
  • Is there a new research question?

Reporting Results - Poster Format
Posters are frequently used as means to display ongoing research and experimental results. At scientific conferences, researchers gather in large auditoriums to display their own work as well as examine the work of others. As individuals circulate throughout the exhibits, they strike up conversations and exchange ideas.

You will display the results of your RUSS data analysis on a poster (Figure below). The lengths of various sections will vary from one poster to the next. Each poster will consist of no more than six 8.5" x 11" pieces of paper glued to tag board (2 rows, 3 columns). Individual pages are arranged to be read from left to right.

poster example

All posters must contain the following, clearly labeled sections:

  • Title - An adaptation of the assigned research question.
  • Authors - Name(s)
  • Introduction - Introduce the research question, the data analyzed, and the analysis plan.
  • Results and Discussion - Display only necessary data. Data presented must be discussed in the text. Discuss data trends, correlations, and outliers.
  • Conclusions - Summarize the data analysis and compose the answer to the research question. Are there additional experiments to conduct? Are there unanswered questions?

Formatting Notes
Text:

All text must be in 24 point font except section titles that appear in 36 point font. Lines should be double spaced with 0.5" margins (top/bottom/left/right). Left justify all text. Use the spelling and grammar checker. Hand written notes and comments are not allowed.

Graphs:

All graphs must be clearly labeled. Use these labels when referring to graphs in text (Example: Figure 1, Figure 2). Graph axis titles must be included and correctly positioned. Hand written notes and comments are unacceptable.

Tables:

Data tables must be properly titled. Columns and rows should be correctly labeled. Hand written notes and comments are unacceptable.


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date last updated: Saturday March 06 2004