Data Analysis: What’s in a P-value??

Biotechnology is an exciting field that involves using living organisms, such as bacteria or eukaryotic cells, to create useful products or solve problems. But to ensure discoveries are meaningful, or to draw valid conclusions, scientists rely on a statistical concept called the p-value. In this post, we’ll break down what p-values are, why they’re important, and how they apply to your research in the biotechnology lab.

What is a P-Value?

P-values, short for “probability values,” are statistical tools used to determine the significance of experimental results. They help scientists decide if the data they’ve collected is strong enough to support their hypotheses or if it could have occurred due to chance.

Formally, “…In statistical science, the p-value is the probability of obtaining a result at least as extreme as the one that was actually observed in the biological or clinical experiment or epidemiological study, given that the null hypothesis is true [1].”

In plain language, the p-value is a measure used in statistics to help us figure out how likely it is to get the results we observed in a scientific experiment, like a biology or clinical study, when we assume a specific idea (null hypothesis) is true. If the p-value is low, it means the results are not likely to be due to chance, making our hypothesis more believable. If the p-value is high, it suggests that the results could easily happen by chance, so our hypothesis might not be supported.

Think of it this way: When you conduct an experiment, you’re trying to answer a question or test a hypothesis. The p-value tells you how likely it is that your results are just due to chance, compared to the possibility that there is a meaningful difference. If the p-value is small, it suggests that your findings are more likely to be significant.

How Do I Calculate the P-Value?

One simple way to calculate the p-value is through the t-test. This statistical tool that helps you decide if the differences you see between your control group and the experimental one is due to random variations or if it’s a genuine difference. For most applications in your lab, the Student’s t-test should be sufficient. Student doesn’t mean “student”; instead it was a pseudonym for William Sealy Gosset, whose employer preferred he publish under a false name.

The Student’s t-test is a valuable tool for researchers and scientists when comparing two groups, making it easier to determine if observed differences are statistically meaningful or just due to random variation. It’s widely used in various fields, from biology and psychology to business and social sciences.

There are three common types of t-tests; we recommend checking out this website for more information about the tests, the assumptions necessary for performing the tests, and when they are appropriate to use. They also show you how to determine the critical value from a t-table based on your chosen level of significance (often set at 0.05 or 5%). This is the threshold that your t-statistic must surpass to be considered significant. The t-test also yields a p-value, which represents the probability that the difference between the two groups is likely real and not due to random chance.

How to Interpret P-Values:

Understanding p-values can be a bit tricky, but the general rule is simple: the smaller the p-value, the more confident you can be in your results. Here are some key points to keep in mind:

• A p-value less than 0.05 (usually denoted as p < 0.05) is often considered significant. It suggests that there is a less than 5% chance that the results occurred by random chance.
• A p-value greater than 0.05 is not considered significant. In this case, the results may be due to chance, and the hypothesis is not supported.

There are debates as to whether using a p-value is too simple and not meaningful for data interpretation [2]. While we do agree that p-values should be evaluated critically, they are an easy metric for showing difference in data in the classroom laboratory

Why Are P-Values Important in Biotechnology?

In biotechnology, researchers often deal with complex data from experiments involving genes, proteins, and other biological molecules. P-values help scientists separate real discoveries from random noise. This is crucial because false positives can lead to incorrect conclusions and wasted resources. For example, making false claims or promoting ineffective products can have serious consequences for people’s health and safety. P-values help ensure that only effective treatments or products are brought to market, protecting the public. Furthermore, pharmaceutical researchers rely on p-values to assess the quality of their experiments. If a study produces results with a low p-value, it’s a good indication that the findings are reliable, which can be especially important in drug development, genetic engineering, and disease diagnosis.

Conclusion:

P-values are like the detectives of the biotechnology world, helping scientists distinguish between real discoveries and random occurrences. By understanding the importance of p-values, high school students can appreciate how these statistical tools contribute to the credibility, quality, and ethical standards in biotechnology research. So, the next time you hear about an exciting biotech breakthrough, remember that behind it, there’s a p-value telling us it’s not just a coincidence!

References:

1. Panagiotakos DB. Value of p-value in biomedical research. Open Cardiovasc Med J. 2008;2:97-9. doi: 10.2174/1874192400802010097. Epub 2008 Nov 18. PMID: 19430522; PMCID: PMC2627527. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2627527/
2. Halsey LG. The reign of the p-value is over: what alternative analyses could we employ to fill the power vacuum? Biol Lett. 2019 May 31;15(5):20190174. doi: 10.1098/rsbl.2019.0174. PMID: 31113309; PMCID: PMC6548726. https://royalsocietypublishing.org/doi/10.1098/rsbl.2019.0174