TL;DR Science: Hypothesis
By Carlos Mercado-Lara
May 20, 2020 · 5 minute read
If I write this blog, then I will increase the amount of my peers who know how to effectively write a hypothesis because of my clear, concise, and helpful advice. For those of you who recall the first time you ever heard about a hypothesis, the classic “if, then, because” statement may have resonated in my statement with you. If not, no worries, I got you covered. In today’s blog, we’ll discuss the importance of having a good hypothesis, how to write a good hypothesis, types of hypothesis, and things to avoid when generating a hypothesis.
An experiment runs on a hypothesis. An experiment lacking a hypothesis is not an experiment at all. An experiment is an experiment because it aims to prove and/or address a hypothesis. For those of you wondering if the hypothesis is more important than the research question you established for your experiment, as best explained by the US national library of medicine, “The primary research question should be driven by the hypothesis rather than the data”(Farrugia 2010). Thinking about this previous statement, it is much easier to go into your data collected knowing what you are looking for rather than seeing what shows up. By having a clear, concise, and well-developed hypothesis, you will spend less time with all the noise that can show up in your data collection and focus on identifying what you have laid out in the hypothesis.
What is a Hypothesis?
At this point in the scientific method (represented in the image below) you have identified the research question or the problem you want to tackle. As best explained by Penn State University, a hypothesis is “A statement about a specific research question, and it outlines the expected result of the experiment.” The keyword here is expected. For example, assuming your research question is “How does fertilizer impact the growth of sunflowers in standard growing conditions? The “expected” result is your best-educated guess or even better, due to the background research you have conducted, you have an idea of what to expect.
Writing a Hypothesis
To start off, your hypothesis should be written in the present tense since it is your research that is currently ongoing.
Looking into the insightful document where Penn State University discusses best practices for formulating a hypothesis, the “PICOT” model offers a great framework to follow when crafting your hypothesis. Here, they are breaking down a hypothesis for Dandelions growing in nitrogen-rich soil.
Referencing my research question regarding sunflower plant growth, my hypothesis statement is as follows: If I add fertilizer over three weeks to sunflowers, then they will grow at a faster rate than sunflowers without fertilizer because of the nutritious supplements that are found in fertilizer.
Breaking down my hypothesis, I have:
Population: Sunflower seed
Interest: Studying the effects of fertilizer growth in sunflower seeds
Comparison: Normal growth of sunflower seeds to fertilizer addition to sunflower seeds
Outcomes: Taller sunflower seeds
Time: Three weeks
If you are able to narrow down and meet these five conditions, then it is safe to say that you have written a good hypothesis. It is always a good idea to have a peer or mentor to read your hypothesis to ensure that it is easy to understand. A good hypothesis will be straightforward and clear enough that a non-expert audience can get a sense of what your experiment entails.
What to Avoid when Writing a Hypothesis
Avoid being too general. A hypothesis should reflect the thoughtful process that you have gone through to write your hypothesis. A bad example is as follows: If I add fertilizer to plants, then they will grow taller because it helps growth. What plants? Over what time? A teacher, peer, or science fair judge might want to hear more about what exactly your experiment is exploring.
Remember, a hypothesis is not a wild guess or an opinion, it should be based on background information or some sort of scientific intuition that has led you to believe that your educated inference is what is expected to happen after you conduct the experiment.
The Null Hypothesis:
Regardless of what scientific field you are looking to get interested in or conducting a project, the null hypothesis is a useful tool when it comes to doing experiments that require statistical analysis. A null hypothesis is essentially rejecting what you observe is due by chance. How do you determine if your results are not merely determined by some random event? That is where the statistical test comes into play to prove that the results are statistically significant and not due to any spontaneous differences that can occur in data. To find out more information regarding statistical significance, check out this amazing website http://www.biostathandbook.com/hypothesistesting.html where all of the information regarding statistical analysis can also be found.
Normally the Null hypothesis is denoted by Ho If the null hypothesis is incorrect, then the alternative hypothesis is proven to be valid. The alternative hypothesis (hint in the name) being the opposite of what the null hypothesis states.
Pro Tip: If you see this notation Ho on any papers, exams, or projects, just remember that it is the null hypothesis. If you are learning about statistical exams make sure to check out Khan academy. We provided a video link to help get you started below.
Keep in mind that it is the hypothesis that you are testing, and therefore it is completely fine if your hypothesis eventually is contradicted! We would not have scientific advancements if it was not for scientists testing new hypotheses after previous hypotheses have failed to explain phenomena. We hope that this blog was helpful and if you have any feedback please make sure to click on the feedback button that can be found at the bottom of the website.
Farrugia, P., Petrisor, B. A., Farrokhyar, F., & Bhandari, M. (2010). Practical tips for surgical research: Research questions, hypotheses and objectives. Canadian journal of surgery. Journal canadien de chirurgie, 53(4), 278–281.
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