Science Fair Wizard
- Pick a topic
- Determine a problem
- Investigate your problem
- Formulate a hypothesis
- Design an experiment
- Test your hypothesis
- Presenting your data
- Using Statistics to Analyze Data
- Writing up your conclusions
- Re-testing your hypothesis
- Write your research paper
- Construct your exhibit
- Prepare your presentation
- Show Time! Pre-science fair checklist
- Submit your paperwork
Step 7C: Writing up your conclusions
Your conclusion should revisit the purpose of your experiment and hypothesis in light of your data analysis. Make sure you address your original question or problem when you interpret your data. Your conclusions should be valid (that is, logical) and limited to the results of the experiment.
Evaluate your data. Explain the effect of experimental error or any procedural changes on your results. Were there variables you couldn’t control for, such as age or other characteristics? Why is this information (your data analysis) important or significant? What is the relevance of your data to everyday life? Science is a process that doesn’t only try to answer a question but generates more questions. What new questions do you have as a result of your experiment?
Does your data support your hypothesis or not? If your hypothesis is incorrect, think about the reasons why this might have happened. This doesn’t mean you didn’t carry out your experiment correctly. Revisit your notes. Did you change anything about the procedure and materials that could explain what happened?
Tip: Use the worksheets you have been completing along the way to refresh your memory about your library research and procedure as you write your conclusion. These worksheets will be helpful when it is time to put together your Science Project Paper.
Scientists at Argonne National Laboratory can help you with your project. ( just ask )
The digital library project
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- Testing Assumptions
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- Predictive Analytics This link opens in a new window
- Quantitative Research Questions
- Null & Alternative Hypotheses
- One-Tail vs. Two-Tail
- Alpha & Beta
- Associated Probability
- Decision Rule
- Statement of Conclusion
- Statistics Group Sessions
Statement of the Conclusion
When writing your results, you’re going to write the decision regarding the null, but you also want to state the results in layman’s terms. Tie the statistical results back to the original claim and interpret what those statistics mean, without all the quantitative jargon.
1) Claim : Females run faster than males. Results of the test : t o > t c Decision : Reject Null Hypothesis. Conclusion : There is sufficient evidence to suggest that females run faster than males.
2) Claim : There is a difference in the highest level of education obtained based on socioeconomic status. Results of the test : p > α Decision : Fail to Reject Null Hypothesis. Conclusion : There is not enough evidence to suggest that highest level of education differs based on socioeconomic status.
3) Claim : The number of calories consumed and the number of hours spent exercising each week are significant predictors of weight. Results of the test : p < α Decision : Reject Null Hypothesis. Conclusion : The results of the hypothesis test suggest that a person’s weight can be predicted given caloric intake and the number of hours spent exercising each week.
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Drawing Conclusions from Statistics
- Describe the role of random sampling and random assignment in drawing cause-and-effect conclusions
One limitation to the study mentioned previously about the babies choosing the “helper” toy is that the conclusion only applies to the 16 infants in the study. We don’t know much about how those 16 infants were selected. Suppose we want to select a subset of individuals (a sample ) from a much larger group of individuals (the population ) in such a way that conclusions from the sample can be generalized to the larger population. This is the question faced by pollsters every day.
Example 1 : The General Social Survey (GSS) is a survey on societal trends conducted every other year in the United States. Based on a sample of about 2,000 adult Americans, researchers make claims about what percentage of the U.S. population consider themselves to be “liberal,” what percentage consider themselves “happy,” what percentage feel “rushed” in their daily lives, and many other issues. The key to making these claims about the larger population of all American adults lies in how the sample is selected. The goal is to select a sample that is representative of the population, and a common way to achieve this goal is to select a random sample that gives every member of the population an equal chance of being selected for the sample. In its simplest form, random sampling involves numbering every member of the population and then using a computer to randomly select the subset to be surveyed. Most polls don’t operate exactly like this, but they do use probability-based sampling methods to select individuals from nationally representative panels.
In 2004, the GSS reported that 817 of 977 respondents (or 83.6%) indicated that they always or sometimes feel rushed. This is a clear majority, but we again need to consider variation due to random sampling . Fortunately, we can use the same probability model we did in the previous example to investigate the probable size of this error. (Note, we can use the coin-tossing model when the actual population size is much, much larger than the sample size, as then we can still consider the probability to be the same for every individual in the sample.) This probability model predicts that the sample result will be within 3 percentage points of the population value (roughly 1 over the square root of the sample size, the margin of error ). A statistician would conclude, with 95% confidence, that between 80.6% and 86.6% of all adult Americans in 2004 would have responded that they sometimes or always feel rushed.
The key to the margin of error is that when we use a probability sampling method, we can make claims about how often (in the long run, with repeated random sampling) the sample result would fall within a certain distance from the unknown population value by chance (meaning by random sampling variation) alone. Conversely, non-random samples are often suspect to bias, meaning the sampling method systematically over-represents some segments of the population and under-represents others. We also still need to consider other sources of bias, such as individuals not responding honestly. These sources of error are not measured by the margin of error.
Cause and Effect
In many research studies, the primary question of interest concerns differences between groups. Then the question becomes how were the groups formed (e.g., selecting people who already drink coffee vs. those who don’t). In some studies, the researchers actively form the groups themselves. But then we have a similar question—could any differences we observe in the groups be an artifact of that group-formation process? Or maybe the difference we observe in the groups is so large that we can discount a “fluke” in the group-formation process as a reasonable explanation for what we find?
Example 2 : A psychology study investigated whether people tend to display more creativity when they are thinking about intrinsic (internal) or extrinsic (external) motivations (Ramsey & Schafer, 2002, based on a study by Amabile, 1985). The subjects were 47 people with extensive experience with creative writing. Subjects began by answering survey questions about either intrinsic motivations for writing (such as the pleasure of self-expression) or extrinsic motivations (such as public recognition). Then all subjects were instructed to write a haiku, and those poems were evaluated for creativity by a panel of judges. The researchers conjectured beforehand that subjects who were thinking about intrinsic motivations would display more creativity than subjects who were thinking about extrinsic motivations. The creativity scores from the 47 subjects in this study are displayed in Figure 2, where higher scores indicate more creativity.
In this example, the key question is whether the type of motivation affects creativity scores. In particular, do subjects who were asked about intrinsic motivations tend to have higher creativity scores than subjects who were asked about extrinsic motivations?
Figure 2 reveals that both motivation groups saw considerable variability in creativity scores, and these scores have considerable overlap between the groups. In other words, it’s certainly not always the case that those with extrinsic motivations have higher creativity than those with intrinsic motivations, but there may still be a statistical tendency in this direction. (Psychologist Keith Stanovich (2013) refers to people’s difficulties with thinking about such probabilistic tendencies as “the Achilles heel of human cognition.”)
The mean creativity score is 19.88 for the intrinsic group, compared to 15.74 for the extrinsic group, which supports the researchers’ conjecture. Yet comparing only the means of the two groups fails to consider the variability of creativity scores in the groups. We can measure variability with statistics using, for instance, the standard deviation: 5.25 for the extrinsic group and 4.40 for the intrinsic group. The standard deviations tell us that most of the creativity scores are within about 5 points of the mean score in each group. We see that the mean score for the intrinsic group lies within one standard deviation of the mean score for extrinsic group. So, although there is a tendency for the creativity scores to be higher in the intrinsic group, on average, the difference is not extremely large.
We again want to consider possible explanations for this difference. The study only involved individuals with extensive creative writing experience. Although this limits the population to which we can generalize, it does not explain why the mean creativity score was a bit larger for the intrinsic group than for the extrinsic group. Maybe women tend to receive higher creativity scores? Here is where we need to focus on how the individuals were assigned to the motivation groups. If only women were in the intrinsic motivation group and only men in the extrinsic group, then this would present a problem because we wouldn’t know if the intrinsic group did better because of the different type of motivation or because they were women. However, the researchers guarded against such a problem by randomly assigning the individuals to the motivation groups. Like flipping a coin, each individual was just as likely to be assigned to either type of motivation. Why is this helpful? Because this random assignment tends to balance out all the variables related to creativity we can think of, and even those we don’t think of in advance, between the two groups. So we should have a similar male/female split between the two groups; we should have a similar age distribution between the two groups; we should have a similar distribution of educational background between the two groups; and so on. Random assignment should produce groups that are as similar as possible except for the type of motivation, which presumably eliminates all those other variables as possible explanations for the observed tendency for higher scores in the intrinsic group.
But does this always work? No, so by “luck of the draw” the groups may be a little different prior to answering the motivation survey. So then the question is, is it possible that an unlucky random assignment is responsible for the observed difference in creativity scores between the groups? In other words, suppose each individual’s poem was going to get the same creativity score no matter which group they were assigned to, that the type of motivation in no way impacted their score. Then how often would the random-assignment process alone lead to a difference in mean creativity scores as large (or larger) than 19.88 – 15.74 = 4.14 points?
We again want to apply to a probability model to approximate a p-value , but this time the model will be a bit different. Think of writing everyone’s creativity scores on an index card, shuffling up the index cards, and then dealing out 23 to the extrinsic motivation group and 24 to the intrinsic motivation group, and finding the difference in the group means. We (better yet, the computer) can repeat this process over and over to see how often, when the scores don’t change, random assignment leads to a difference in means at least as large as 4.41. Figure 3 shows the results from 1,000 such hypothetical random assignments for these scores.
Only 2 of the 1,000 simulated random assignments produced a difference in group means of 4.41 or larger. In other words, the approximate p-value is 2/1000 = 0.002. This small p-value indicates that it would be very surprising for the random assignment process alone to produce such a large difference in group means. Therefore, as with Example 2, we have strong evidence that focusing on intrinsic motivations tends to increase creativity scores, as compared to thinking about extrinsic motivations.
Notice that the previous statement implies a cause-and-effect relationship between motivation and creativity score; is such a strong conclusion justified? Yes, because of the random assignment used in the study. That should have balanced out any other variables between the two groups, so now that the small p-value convinces us that the higher mean in the intrinsic group wasn’t just a coincidence, the only reasonable explanation left is the difference in the type of motivation. Can we generalize this conclusion to everyone? Not necessarily—we could cautiously generalize this conclusion to individuals with extensive experience in creative writing similar the individuals in this study, but we would still want to know more about how these individuals were selected to participate.
Statistical thinking involves the careful design of a study to collect meaningful data to answer a focused research question, detailed analysis of patterns in the data, and drawing conclusions that go beyond the observed data. Random sampling is paramount to generalizing results from our sample to a larger population, and random assignment is key to drawing cause-and-effect conclusions. With both kinds of randomness, probability models help us assess how much random variation we can expect in our results, in order to determine whether our results could happen by chance alone and to estimate a margin of error.
So where does this leave us with regard to the coffee study mentioned previously (the Freedman, Park, Abnet, Hollenbeck, & Sinha, 2012 found that men who drank at least six cups of coffee a day had a 10% lower chance of dying (women 15% lower) than those who drank none)? We can answer many of the questions:
- This was a 14-year study conducted by researchers at the National Cancer Institute.
- The results were published in the June issue of the New England Journal of Medicine , a respected, peer-reviewed journal.
- The study reviewed coffee habits of more than 402,000 people ages 50 to 71 from six states and two metropolitan areas. Those with cancer, heart disease, and stroke were excluded at the start of the study. Coffee consumption was assessed once at the start of the study.
- About 52,000 people died during the course of the study.
- People who drank between two and five cups of coffee daily showed a lower risk as well, but the amount of reduction increased for those drinking six or more cups.
- The sample sizes were fairly large and so the p-values are quite small, even though percent reduction in risk was not extremely large (dropping from a 12% chance to about 10%–11%).
- Whether coffee was caffeinated or decaffeinated did not appear to affect the results.
- This was an observational study, so no cause-and-effect conclusions can be drawn between coffee drinking and increased longevity, contrary to the impression conveyed by many news headlines about this study. In particular, it’s possible that those with chronic diseases don’t tend to drink coffee.
This study needs to be reviewed in the larger context of similar studies and consistency of results across studies, with the constant caution that this was not a randomized experiment. Whereas a statistical analysis can still “adjust” for other potential confounding variables, we are not yet convinced that researchers have identified them all or completely isolated why this decrease in death risk is evident. Researchers can now take the findings of this study and develop more focused studies that address new questions.
Explore these outside resources to learn more about applied statistics:
- Video about p-values: P-Value Extravaganza
- Interactive web applets for teaching and learning statistics
- Inter-university Consortium for Political and Social Research where you can find and analyze data.
- The Consortium for the Advancement of Undergraduate Statistics
Think It Over
- Find a recent research article in your field and answer the following: What was the primary research question? How were individuals selected to participate in the study? Were summary results provided? How strong is the evidence presented in favor or against the research question? Was random assignment used? Summarize the main conclusions from the study, addressing the issues of statistical significance, statistical confidence, generalizability, and cause and effect. Do you agree with the conclusions drawn from this study, based on the study design and the results presented?
- Is it reasonable to use a random sample of 1,000 individuals to draw conclusions about all U.S. adults? Explain why or why not.
CC licensed content, Original
- Modification, adaptation, and original content. Authored by : Pat Carroll and Lumen Learning. Provided by : Lumen Learning. License : CC BY: Attribution
CC licensed content, Shared previously
- Statistical Thinking. Authored by : Beth Chance and Allan Rossman, California Polytechnic State University, San Luis Obispo. Provided by : Noba. Located at : http://nobaproject.com/modules/statistical-thinking . License : CC BY-NC-SA: Attribution-NonCommercial-ShareAlike
- The Replication Crisis. Authored by : Colin Thomas William. Provided by : Ivy Tech Community College. License : CC BY: Attribution
related to whether the results from the sample can be generalized to a larger population.
the collection of individuals on which we collect data.
a larger collection of individuals that we would like to generalize our results to.
using a probability-based method to select a subset of individuals for the sample from the population.
the expected amount of random variation in a statistic; often defined for 95% confidence level.
using a probability-based method to divide a sample into treatment groups.
the probability of observing a particular outcome in a sample, or more extreme, under a conjecture about the larger population or process.
related to whether we say one variable is causing changes in the other variable, versus other variables that may be related to these two variables.
General Psychology Copyright © by OpenStax and Lumen Learning is licensed under a Creative Commons Attribution 4.0 International License , except where otherwise noted.
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Writing a Research Paper Conclusion | Step-by-Step Guide
Published on October 30, 2022 by Jack Caulfield . Revised on April 13, 2023.
- Restate the problem statement addressed in the paper
- Summarize your overall arguments or findings
- Suggest the key takeaways from your paper
The content of the conclusion varies depending on whether your paper presents the results of original empirical research or constructs an argument through engagement with sources .
Table of contents
Step 1: restate the problem, step 2: sum up the paper, step 3: discuss the implications, research paper conclusion examples, frequently asked questions about research paper conclusions.
The first task of your conclusion is to remind the reader of your research problem . You will have discussed this problem in depth throughout the body, but now the point is to zoom back out from the details to the bigger picture.
While you are restating a problem you’ve already introduced, you should avoid phrasing it identically to how it appeared in the introduction . Ideally, you’ll find a novel way to circle back to the problem from the more detailed ideas discussed in the body.
For example, an argumentative paper advocating new measures to reduce the environmental impact of agriculture might restate its problem as follows:
Meanwhile, an empirical paper studying the relationship of Instagram use with body image issues might present its problem like this:
“In conclusion …”
Avoid starting your conclusion with phrases like “In conclusion” or “To conclude,” as this can come across as too obvious and make your writing seem unsophisticated. The content and placement of your conclusion should make its function clear without the need for additional signposting.
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Having zoomed back in on the problem, it’s time to summarize how the body of the paper went about addressing it, and what conclusions this approach led to.
Depending on the nature of your research paper, this might mean restating your thesis and arguments, or summarizing your overall findings.
Argumentative paper: Restate your thesis and arguments
In an argumentative paper, you will have presented a thesis statement in your introduction, expressing the overall claim your paper argues for. In the conclusion, you should restate the thesis and show how it has been developed through the body of the paper.
Briefly summarize the key arguments made in the body, showing how each of them contributes to proving your thesis. You may also mention any counterarguments you addressed, emphasizing why your thesis holds up against them, particularly if your argument is a controversial one.
Don’t go into the details of your evidence or present new ideas; focus on outlining in broad strokes the argument you have made.
Empirical paper: Summarize your findings
In an empirical paper, this is the time to summarize your key findings. Don’t go into great detail here (you will have presented your in-depth results and discussion already), but do clearly express the answers to the research questions you investigated.
Describe your main findings, even if they weren’t necessarily the ones you expected or hoped for, and explain the overall conclusion they led you to.
Having summed up your key arguments or findings, the conclusion ends by considering the broader implications of your research. This means expressing the key takeaways, practical or theoretical, from your paper—often in the form of a call for action or suggestions for future research.
Argumentative paper: Strong closing statement
An argumentative paper generally ends with a strong closing statement. In the case of a practical argument, make a call for action: What actions do you think should be taken by the people or organizations concerned in response to your argument?
If your topic is more theoretical and unsuitable for a call for action, your closing statement should express the significance of your argument—for example, in proposing a new understanding of a topic or laying the groundwork for future research.
Empirical paper: Future research directions
In a more empirical paper, you can close by either making recommendations for practice (for example, in clinical or policy papers), or suggesting directions for future research.
Whatever the scope of your own research, there will always be room for further investigation of related topics, and you’ll often discover new questions and problems during the research process .
Finish your paper on a forward-looking note by suggesting how you or other researchers might build on this topic in the future and address any limitations of the current paper.
Full examples of research paper conclusions are shown in the tabs below: one for an argumentative paper, the other for an empirical paper.
- Argumentative paper
- Empirical paper
While the role of cattle in climate change is by now common knowledge, countries like the Netherlands continually fail to confront this issue with the urgency it deserves. The evidence is clear: To create a truly futureproof agricultural sector, Dutch farmers must be incentivized to transition from livestock farming to sustainable vegetable farming. As well as dramatically lowering emissions, plant-based agriculture, if approached in the right way, can produce more food with less land, providing opportunities for nature regeneration areas that will themselves contribute to climate targets. Although this approach would have economic ramifications, from a long-term perspective, it would represent a significant step towards a more sustainable and resilient national economy. Transitioning to sustainable vegetable farming will make the Netherlands greener and healthier, setting an example for other European governments. Farmers, policymakers, and consumers must focus on the future, not just on their own short-term interests, and work to implement this transition now.
As social media becomes increasingly central to young people’s everyday lives, it is important to understand how different platforms affect their developing self-conception. By testing the effect of daily Instagram use among teenage girls, this study established that highly visual social media does indeed have a significant effect on body image concerns, with a strong correlation between the amount of time spent on the platform and participants’ self-reported dissatisfaction with their appearance. However, the strength of this effect was moderated by pre-test self-esteem ratings: Participants with higher self-esteem were less likely to experience an increase in body image concerns after using Instagram. This suggests that, while Instagram does impact body image, it is also important to consider the wider social and psychological context in which this usage occurs: Teenagers who are already predisposed to self-esteem issues may be at greater risk of experiencing negative effects. Future research into Instagram and other highly visual social media should focus on establishing a clearer picture of how self-esteem and related constructs influence young people’s experiences of these platforms. Furthermore, while this experiment measured Instagram usage in terms of time spent on the platform, observational studies are required to gain more insight into different patterns of usage—to investigate, for instance, whether active posting is associated with different effects than passive consumption of social media content.
If you’re unsure about the conclusion, it can be helpful to ask a friend or fellow student to read your conclusion and summarize the main takeaways.
- Do they understand from your conclusion what your research was about?
- Are they able to summarize the implications of your findings?
- Can they answer your research question based on your conclusion?
You can also get an expert to proofread and feedback your paper with a paper editing service .
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The conclusion of a research paper has several key elements you should make sure to include:
- A restatement of the research problem
- A summary of your key arguments and/or findings
- A short discussion of the implications of your research
No, it’s not appropriate to present new arguments or evidence in the conclusion . While you might be tempted to save a striking argument for last, research papers follow a more formal structure than this.
All your findings and arguments should be presented in the body of the text (more specifically in the results and discussion sections if you are following a scientific structure). The conclusion is meant to summarize and reflect on the evidence and arguments you have already presented, not introduce new ones.
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Caulfield, J. (2023, April 13). Writing a Research Paper Conclusion | Step-by-Step Guide. Scribbr. Retrieved November 13, 2023, from https://www.scribbr.com/research-paper/research-paper-conclusion/
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The second to last step in conducting a research study is to interpret the findings in the Discussion section, draw conclusions, and make recommendations. It is important that everything in this last section is based off of the results of the data analysis. In an empirical research study, the conclusions and recommendations must be directly related to the data that was collected and analyzed. Simply put, look at the key topics in the conclusion and recommendations. If that topic was not precisely assessed by the questionnaire, then you cannot draw a conclusion or make a recommendation about that topic. A paper can only make valid conclusions and recommendations on those variables that the study has empirical data to support.
For example, almost every single paper written in Nigeria that I read recommends that the government should provide more teaching materials and increase the salaries of teachers. However, better instructional materials and adequate pay are rarely even distantly related to the topic of the paper! Draw conclusions and make recommendations only directly related to the purpose and results of the study. Everybody in Nigeria knows that schools need more materials and teachers need higher salaries Draw conclusions and make recommendations that are meaningful, unique, and relate to the results of the study.
Most papers require five different sections for the Discussion, although the order may vary depending on the requirements of the paper. Each section is described separately below.
This section might be called Discussion or it might be called Summary of Findings. The purpose of this section is to highlight the major statistical findings from the results section and interpret them. First, restate the overall purpose of the study. Then explain the main finding as related to the overall purpose of the study. Next, summarize other interesting findings from the results section. Explain how the statistical findings relate to that purpose of the study. One way to do this is to take every research question and hypothesis in turn and explain in plain terms what the statistical results mean. Also describe how the results are related to education in general. All explanations must be supported by the results of the data analysis.
Generally, the Discussion section does not need to include any numbers. No statistics need to be repeated from the results, nor does the discussion need to refer to table numbers. Instead, simply explain the results in language that is easy for a non-researcher to understand.
Also try to integrate the findings into the results of other research studies. An example paragraph from a Discussion section is given below:
Next, give recommendations based on the results of the study. What practical steps can educators take to implement the key findings of the research study? Remember, these recommendations must be supported by the statistical findings from the data analysis. If the statistical results found that a new teaching program improves mathematical exam scores, then the only valid recommendation that can be made is that the new teaching program should be implemented in order to improve exam scores. However, if the data analysis found that the new teaching program does not improve mathematical exam scores, then the researcher cannot conclude that the new teaching program should be implemented, because the program was found to be ineffective in improving exam scores.
Educators can only change their own behavior; they cannot change the government. Therefore, the most beneficial recommendations will be ones that educators themselves can implement.
Below is a sample recommendation. Notice how the first sentence provides the empirical support for the recommendation.
After the recommendations have been written, reread each recommendation. Consider which statistical result from the results section supports that recommendation. If there is no statistical result to support the recommendation, then it must be canceled.
All studies have limitations in terms of the sample, measurement or manipulation of key variables, and procedure for data collection. This section should report the limitations that resulted from the research methods. How could the research be conducted with a different research design? How may the participants and sampling techniques not be representative of the target population? How might the target population be limited? How were the instruments inadequate? Were there any problems with the treatment? What problems resulted from the study's procedures? What other unexpected problems arose in the data collection?
I frequently read that the study was limited by time, money, or other resources. However, every single research study ever conducted in the history of this world was limited by money, resources, and time. These factors are external to the study and should not be mentionedd.
A sample Limitations section is given below.
Every research study provides one or two answers about education, but also opens the door for five to ten additional questions. Based on the Discussion/Summary of Findings and Limitations of the study, what additional research should be conducted? What questions arose because of the major finding of your study? How can other research studies improve over the limitations that were described in the Limitations section? A sample of Suggestions for Further Research section is below.
The final section of the paper is the Conclusion section. Briefly summarize the overall conclusion of the data analysis based on the purpose of the study. Also explain the importance of the major finding to educational practice. An example conclusion is given below.
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There are different types of essays: narrative, persuasive, compare\contrast, definition and many many others. They are written using a required citation style, where the most common are APA and MLA. We want to share some of the essays samples written on various topics using different citation styles.
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