How to write a scientific abstract
Abstracts are about summarising your data. Start with a title, does not need to be catchy, just needs to make sense! You want to let the reader know exactly what your paper is going to be about in roughly a paragraph. Therefore, it is important to withdraw the key points. In your first sentence, you want to briefly introduce the topic, maybe make a bold statement to engage the reader. It does not need to be detailed, as that is what the rest of the paper is for. Your next sentence you want to set out the aim of what you wanted to investigate, this needs to be to the point. It needs to be clear what you are going to investigate and what conclusions you want to make. You then briefly describe the methods used to answer your objectives, then what the key results were and then draw out your main conclusion. It does not matter if your abstract is brief, the paper itself can be pretty hefty! But remember, it’s not a blurb or a movie trailer, you don’t need to keep secrets or hide the plot twists, anything that is highly significant should probably go in your abstract.
An example Abstract
Sex differences in coagulation and packed cell volume in a group of undergraduates at a British University
A standardized examination of packed cell volume (PCV) and international normalised ratio (INR) is used for testing cell count and coagulation parameters. The objective of this study is to examine whether there is a significant difference between male and female PCV and INR to determine whether the null hypothesis can be rejected. Haematology is concerned with studying blood. PCV values measure the proportion of blood that is made up of cells. INR values show how long the blood takes to clot. 132 students took blood samples through pricking fingers using Unistick lancets, the blood was taken up into a microhaematocrit tube to then be centrifuged. PCV values were calculated from samples. INR meters were used to show INR values of finger prick blood samples. For PCV values, a statistical test that used the measurements to compare the differences between two groups is required. A two-sample t- test is performed to determine whether the two populations means are equal. The assumption is made that the two populations have equal variances. For INR values, a non- parametric alternative test is required to test the sample means as the assumptions of the t- test are not met. A Mann-Whitney U test is performed to demonstrate whether INR means in both populations are equal. The null hypothesis states that there is no significant difference in the means of both sample populations. An alternative hypothesis is that there is a significant difference in the means of sample populations. For PCV samples, the F test statistic is 1.95, thus the assumption of equal variances is satisfied. For PCV values, the t test statistic is 2.96 and the P value is 0.0033, therefore the null hypothesis is rejected and the alternative hypothesis is accepted at the 0.05 significance level. For INR values, the p value is 0.20766 and so the null hypothesis can be accepted as there is a significant difference detected at the 0.05 significance level. To conclude there is a difference in PCV means and there is not a significant difference in INR values between male and female sample populations at this University.