Template paper and judging criteria
Your paper should consist of no more than 3 pages of concisely written single-spaced text (tables and graphs are included in the 3 page limit; however the references and an (optional) appendix are not). Furthermore, the title and abstract should be on a separate page (with no other information listed). You should use Arial, 11 pt font, single spaced with standard 1 inch margins. Each section (or subsection) should receive a heading.
While there are no specific structural limitations to the paper, one possible structure is suggested below.
1. Title (on title page)
2. Abstract (on title page)
3. Background and Significance (part of 3 pages)
4. Methods (part of 3 pages)
5. Results (part of 3 pages)
6. Discussion/Conclusions (part of 3 pages)
7. References (not included in the 3 page limit)
Give an informative title to your project.
Assessment: Does the title give an accurate preview of what the paper is about? Is it informative, specific and precise?
The abstract provides a brief summary of the entire paper (background, methods, results and conclusions). The suggested length is no more than 150 words. This allows you approximately 1 sentence (and likely no more than two sentences) summarizing each of the following sections. Typically, abstracts are the last thing you write.
Assessment: Are the main points of the paper described clearly and succinctly?
3. Background and significance
In this section you are providing the background of the research area and arguing why it is interesting and significant. This section relies heavily on literature review (prior research done in this area and facts that argue why the research is important). This whole section should provide the necessary background leading up to a presentation (in the last few sentences of this section) of the research hypotheses that you will be testing in your study. Well-accepted facts and/or referenced statements should serve as the majority of content of this section. Typically, the background and significance section starts very broad and moves towards the specific area/hypotheses you are testing.
-Does the background and significance have a logical organization? Does it move from the general to the specific?
- Has sufficient background been provided to understand the paper? How does this work relate to other work in the scientific literature?
- Has a reasonable explanation been given for why the research was done? Why is the work important? Why is it relevant?
- Does this section end with statements about the hypothesis/goals of the paper?
a. Data collection. Explain how the data was collected/experiment was conducted. Additionally, you should provide information on the individuals who participated to assess representativeness. Non-response rates and other relevant data collection details should be mentioned here if they are an issue. However, you should not discuss the impact of these issues here---save that for the limitations section.
b. Variable creation. Detail the variables in your analysis and how they are defined (if necessary). For example, if you created a combined (frequency times quantity) drinking variable you should describe how. If you are talking about gender no further explanation is really needed.
c. Analytic Methods. Explain the statistical procedures that will be used to analyze your data. E.g. Boxplots are used to illustrate differences in GPA across gender and class standing. Correlations are used to assess the impacts of gender and class standing on GPA.
Assessment: Could the study be repeated based on the information given here? Is the material organized into logical categories (like the one’s above)?
Typically, results sections start with descriptive statistics, e.g. what percent of the sample is male/female, what is the mean GPA overall, in the different groups, etc. Figures can be nice to illustrate these differences! However, information presented must be relevant in helping to answer the research question(s) of interest. Typically, inferential (i.e. hypothesis tests) statistics come next. Tables can often be helpful for results from multiple regression. Do not give computer output here! This should look like a peer-reviewed journal article results section. Tables and figures should be labeled, embedded in the text, and referenced appropriately. The results section typically makes for fairly dry reading. It does not explain the impact of findings, it merely highlights and reports statistical information.
- Is the content appropriate for a results section? Is there a clear description of the results?
- Are the results/data analyzed well? Given the data in each figure/table is the interpretation accurate and logical? Is the analysis of the data thorough (anything ignored?)
- Are the figures/tables appropriate for the data being discussed? Are the figure legends and titles clear and concise?
Restate your objective and draw connections between your analyses and objective. In other words, how did (or didn’t) you answer/address your objective. Place these all in the larger scope of previous research on your topic (i.e. what you found from the literature review), that is, how do your findings help the field move forward? Talk about the limitations of your findings and possible areas for future research to better investigate your research question. End with a concluding sentence or two that summarizes your key findings and impact on the field.
- Does the author clearly state whether the results answer the question (support or disprove the hypothesis)?
- Were specific data cited from the results to support each interpretation? Does the author clearly articulate the basis for supporting or rejecting each hypothesis?
- Does the author adequately relate the results of the current work to previous research?
Assessment: Are the references appropriate and of adequate quality? Are the references citied properly (both in the text and at the end of the paper)?
Some general criteria that the judges may use include:
1. Description of the data source
2. Accuracy of data analysis
3. Accuracy of conclusions and discussion
4. Overall clarity and presentation
5. Originality and significance of the study
6. Writing quality and organization of the paper
Overview and Introduction
This handout explains how to write with statistics including quick tips, writing descriptive statistics, writing inferential statistics, and using visuals with statistics.
Last Edited: 2011-09-28 11:29:36
Statistics is a tricky business. The casual reader doesn't understand statistics in any great depth, while the experienced reader often knows a lot about the subject. Balancing between these two extremes is often difficult, and far from natural. The following resource is meant as a guide to writing statistics.
This guide is not meant to teach you statistics, but rather how to use statistics more effectively in your writing. This guide is designed to help you understand both how to write using other people's statistics, and how to write using your own statistics. If you want to learn how to interpret statistics, then take a course taught by a professional. For an excellent beginner's textbook, see Introduction to the Practice of Statistics by David S. Moore and George P. McCabe.
What is a Statistic?
In the casual sense, a statistic is any number that describes a group of objects. There are two main categories of statistics, descriptive and inferential.
- Descriptive: Statistics that merely describe the group they belong to.
- Inferential: Statistics that are used to draw conclusions about a larger group of people.
Examples of Descriptive Statistics
The class did well on its first exam, with a mean (average) score of 89.5% and a standard deviation of 7.8%.
This season, the Big High School Hockey Team scored a mean (average) of 2.3 goals per game.
Many times, however this group of objects is a smaller subset of a larger group. By examining the smaller subset, it is often thought that information can be inferred upon the larger population. This is the basis of inferential statistics.
Examples of Inferential Statistics
According to our recent poll, 43% of Americans brush their teeth incorrectly.
Our research indicates that only 33% of people like purple cars.
In these last two examples, the researchers have not studied all people, they have studied a small group of people, and are generalizing the results to lots of people. This is known as inferential statistics, because you are inferring properties about a large group from a smaller group. As a statistician or a researcher, it is your hope that this smaller group is representative of the larger group, and that the two groups behave the same way. If they do not, then your inference may not be correct.
If you merely want to describe the data that you have for one single group, then you are using descriptive statistics. If you want to say something about a larger group, or you want your reader to infer something about a larger group, then you need to use inferential statistics. It is important to understand the difference between these two because how you use a statistic depends on what type of statistic it is.