How to Analyze Scientific & Technical Texts

Instructor: Amanda Robb

Amanda holds a Masters in Science from Tufts Medical School in Cellular and Molecular Physiology. She has taught high school Biology and Physics for 8 years.

In this lesson, we'll be learning about skills for analyzing scientific and technical texts. From a lens of physical science, you'll be able to evaluate the validity of scientific texts to reach your own conclusions about their research.

What Is a Scientific Text?

You just started your first college-level chemistry class. On the first day, the professor assigns you two scientific articles to read from the Journal of American Chemical Society. He asks you to evaluate which has more reliable results and to provide evidence for your answer during the next class. You've only read textbooks in high school which tend to have common language and easy-to-read diagrams. Feeling intimidated, you turn to Study.com for help.

Today, we'll be going through four steps to analyze a scientific text. There are five main sections to any technical paper: the introduction, materials, methods, results, and conclusion. We'll learn how to analyze each section and make conclusions about the article. After this lesson, your assignment should be a breeze.

Annotation

The first step to reading a scientific article is to read the abstract, or summary. This will give you a general overview of what the study was about and what conclusions were found. This will give you a focus as you read.

Next, you'll read the introduction which includes important background information. While you read, you should underline important information and write notes in the margins about abbreviations used (which are quite plentiful in scientific articles) or any important definitions. This strategy is called annotation.

Annotation can be done on the document or on a separate paper
annotation

Continue annotating as you read each section. Make notes about anything you notice in the materials and methods sections. Are you familiar with any of the methods? Are there any methods you need to research further? If you don't understand something, make a note so you can get more information. Often times, students gloss over parts of a scientific article they don't understand, but this leaves gaps in understanding and will hinder your ability to analyze the data.

In the results section, circle important findings. Make notes about what the graphs mean. Don't leave anything unread.

Once you've read the article, read it again. Do you have a general idea of what the purpose of the study was, how they conducted it, and what they found? If you can't answer these questions in a few sentences, review the article again. Scientific articles take a lot of time to read. It's okay to read it multiple times.

Analysis of Methods

The methods section explains how the researchers did the experiment. This is a very important part for analyzing a study. If the scientists didn't do the experiment in a way that generates reliable data, the study shouldn't be considered valid, or correct. If you're familiar with the methods, you can check if they match up with yours. If you are not, you might need to spend some time researching what other scientists have done.

All experiments should have a negative control, or a sample that is run through the same tests but expects no results. If the negative control does get a result, the results should be considered invalid.

The negative control is not expected to have a result shown in green, while the treated sample turns yellow indicating a significant difference
negative control

Analysis of Results

Once you've thoroughly reviewed the methods section, it's time to see if the results make sense. You should have some notes from your annotation. Now, it's time to look at their statistics for the data. Statistics tell you if a result is significant and did not occur due to chance. All results should be statistically significant for them to be valid. You can see how significant the results are by looking at the graphs of data. Every graph should include error bars, which show the variability within the data. If the error bars for two samples overlap, the results are not significant and should not be considered valid.

Error bars indicate statistical significance of the data
error bars

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