Data analysis paragraph example

Data analysis paragraph example

Now let’s consider the basic outline of the data analysis report in more detail: 1. Introduction. Good features for the Introduction include: •Summary of the study and data, as well as any relevant substantive context, background, or framing issues. •The “big questions” answered by your data File Size: 19KB Analytical paragraphs Examples, samples Question 1: Below is a graph given showing birth and death rates in a country from to Write an analytical paragraph ( words). Answer 1: The graph shows birth and death rates starting from till Since , the birth rate has remained more than the death rate until For example: “Neighborhood was included as a categorical predictor in the model because Figure 2 indicated clear differences in price across the neighborhoods.”. Sometimes your Data and Model section will contain plots or tables, and sometimes it won’t

Sample Data Analysis Paper — HCC Learning Web

Sandra Slutz, PhD, Staff Scientist, data analysis paragraph example, Science Buddies Kenneth L. Hess, Founder and President, Science Buddies. Whether your goal is to present your findings to the public or publish your research in a scientific journal, it is imperative that data from advanced science projects be rigorously analyzed.

Without careful data analysis to back up your conclusions, the results of your scientific research won't be taken seriously by other scientists.

The sections below discuss techniques, tips, and resources for thorough scientific data analysis. Although this guide will mention various data-analysis principles and statistical tests, it is not meant to be an exhaustive textbook.

Instead, you're encouraged to use this guide as a means of familiarizing yourself with the general principles of data analysis. Once you're familiar with the concepts, we encourage you to continue your exploration of the topics most relevant to your science project using the references listed in the Bibliography, as well as personal resources, such as your mentor and other science and math professionals, including your teachers. We also encourage you to read our accompanying articles about the Experimental Design for Advanced Science Projects and the Increasing the Ability of an Experiment to Measure an Effect.

When used collectively, the information in these three articles will put you on the path towards a well-thought-out, top-quality research project. None of these things could be further from the truth.

Data analysis is an ongoing process in a research project. Planning what kinds of analyses you're going to perform with your data is a critical part of designing your experiments.

If you skip this step, you might find yourself with insufficient data to draw a meaningful conclusion. For more details on how successful data analysis and good experimental design are co-dependent, see the Science Buddies guide to Experimental Design for Advanced Science Projects, data analysis paragraph example.

Once you have designed your experiments and are carrying them out, it can be wise to do some data analysis, even while you are collecting your data, to ensure that the observations are within expected parameters.

For example, you might calculate the yield of a DNA extraction in the midst of an experiment to make sure the procedure worked well before moving on to the next step.

This kind of analysis prevents you from wasting valuable experimental time if something is wrong with your experimental procedure, and can eliminate confusion later over aberrant data. Data should also be analyzed between independent replicates in case the trends or observations from one experimental repeat offers insights on how to better design additional repeats. Although it might be tempting to quickly plug your data into a spreadsheet, create a graph, print out the basic corresponding statistics, data analysis paragraph example celebrate your project as "finished," this methodology might lead you to miss relevant information.

Instead, you should plan to spend a good chunk of time "playing" with your data. The more variables you test, the longer this "playing" takes. By looking at the data from various perspectives, trying different ways of organizing the data and representing it visually and mathematically, you might stumble upon connections or trends of which you were unaware when starting the project. Lastly, it is always important to not just have your data stand alone, but to put it into context. Simply put, expressing your data relative to other data is much more enlightening.

For example, the data in a study on the height of Japanese male professional basketball players might show that the average player height is 6 feet 5 inches. This data becomes more informative if you compare it to the average height of Japanese males, 5 feet 7 inches, thus allowing you to conclude that in Japan, basketball players are likely to be 14 percent taller than the average male.

Similarly, if your research is a replicate of previous work or a methodological improvement on a process, it is critical to analyze your data in direct comparison with the previously published data. Every field has standards and norms for how to analyze data. Researchers, and others in the field who are reviewing your research, will expect data analysis paragraph example to be aware of and to emulate data analysis paragraph example standards where appropriate.

That isn't to say they disapprove of new innovations or techniques—just be sure you're able to explain the advantages of your analytical methods over methods that are traditional to the field. How do you conclude what the standard analytical techniques data analysis paragraph example in your field? The best way is to take a careful look at a wide range of papers in your field. Pay special attention to papers that are collecting the same types of data as you are. Make note of things like:, data analysis paragraph example.

Once you're familiar with the types of analyses common to data analysis paragraph example field, you can pick and choose the ones that make the most sense in the context of your research project.

Generally speaking, scientific data analysis usually involves one or more of following three tasks:. Tables are used to organize data in one place. Relevant column and row headings facilitate finding information quickly. One of the greatest advantages of tables is that when data is organized, it can be easier to spot trends and anomalies. Another advantage is their data analysis paragraph example. Tables can be used to encapsulate either quantitative or data analysis paragraph example data, or even a combination of the two.

Data can be displayed in its raw form, or organized into data summaries with corresponding statistics. Graphs are a visual means of representing data. They allow complex data to be represented in a way that is easier to spot trends by eye.

You might think of graphs as the primary way to present your data to others; although graphs are excellent ways of doing that see the Science Buddies guide about Data Presentation Tips for Advanced Science Competitions for more detailsdata analysis paragraph example, they're also a good analytical mechanism, data analysis paragraph example.

The process of manipulating the data into different visual forms often draws your attention to different aspects of data analysis paragraph example data and expands your thinking about it.

In the process, you may stumble upon a pattern or trend that suggests something new about your science project that you hadn't thought of before. Seeing your data in different graphical formats might highlight new conclusions, new questions, data analysis paragraph example, or the need to go and gather additional data, data analysis paragraph example.

It can also help you to identify outliers. These are data points that appear to be inconsistent with the other data points. Outliers can be the results of experimental error, like a malfunctioning measurement tool, data-entry errors, or rare events that actually happened like a 70°F day in January in Montanabut don't reflect what is normal.

When statistically analyzing your data, it is important to identify outliers and deal with them see the Bibliography, below, for articles discussing how to deal with outliers so that they don't disproportionally affect your conclusions. Identifying outliers also allows you to go back and assess whether they reflect rare events and whether such events are informative to your overall scientific conclusions.

If you are unsure of what kinds of graphs might best encapsulate your data, go back to published scientific articles with similar types of data. Observe how the authors graph and represent their data. Try analyzing your data using the same methods, data analysis paragraph example. Statistics are the third general way of examining data.

There are two broad categories of statistics: descriptive statistics and inferential statistics. Descriptive statistics are used to summarize the data and include things like average, data analysis paragraph example, range, standard deviation, and frequency.

Inferential statistics rely on samples the data you collect to make inferences about a population. They're used to determine whether it is data analysis paragraph example to draw general conclusions about a population, data analysis paragraph example, or predictions about the future based on your experimental data.

Inferential statistics data analysis paragraph example a wide variety of statistical concepts, such as: hypothesis testing, correlation, estimation, and modeling. Beyond the basic descriptive statistics like mean, mode, and average, you might not have had much exposure to statistics. So how do you know what statistical tests to apply to your data?

A good starting place is to refer back to published scientific articles in your field. The "Methods" sections of papers with similar types of data sets will discuss the statistical tests the authors used. Other tests might be referred to within data tables or figures. Try evaluating your data using similar tests. You might also find it useful to consult with statistical textbooks, math teachers, your science project mentor, and other science or engineering professionals.

The Bibliography, data analysis paragraph example, also contains a list of resources for learning more about statistics and their applications. Menu Project Ideas. View Site Map. Science Projects. Grade Levels. Middle School Sixth Grade Seventh Grade Eighth Grade. High School Ninth Grade Tenth Grade Eleventh Grade Twelfth Grade. Physical Science. Earth and Environmental Science. Behavioral and Social Science. Science Projects Topic Selection Wizard By Area of Science By Grade Level Science Project Kits.

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How to write the Results part 1

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Data Analysis Examples

For example: “Neighborhood was included as a categorical predictor in the model because Figure 2 indicated clear differences in price across the neighborhoods.”. Sometimes your Data and Model section will contain plots or tables, and sometimes it won’t Data Analysis Examples. The pages below contain examples (often hypothetical) illustrating the application of different statistical analysis techniques using different statistical packages. Each page provides a handful of examples of when the analysis might be used along with sample data, an example analysis and an explanation of the output, followed by references for more information QUALITATIVE ANALYSIS "Data analysis is the process of bringing order, structure and meaning to the mass of collected data. It is a messy, ambiguous, time-consuming, creative, and fascinating process. It does not proceed in a linear fashion; it is not neat. Qualitative data analysis is a search for general statements about relationships among