Conferences are an excellent opportunity to hear about the latest news in your field. They are also a great chance to meet like-minded people, share experiences, discuss ideas and gain inspiration. Friendships, collaborations, new research directions, invitations and job offers can all arise from conversations at conferences. Conferences range from small regional or national gatherings to huge international meetings. They may address a particular topic or may be a more general meeting of a learned society, including symposia on a variety of topics as well as society business meetings. Conferences may have only one session, with all delegates in the same room at the same time or may have multiple concurrent sessions in a conference venue where all the rooms and corridors look the same and it’s very easy to get lost. Most conferences include keynote or plenary presentations by major researchers in the field. This is a great chance to meet the people whose articles you have read and admired. In this chapter, I cover preparing and submitting an abstract, then attending a conference. Next, I provide general advice on presentations, then cover preparing and presenting oral and poster presentations. I end with conference etiquette.

Good research design includes choosing what to measure and how to measure it. We can’t measure everything. Fortunately, clear predictions dictate the measurements we need to make to test them. This chapter provides general advice on methods, then covers the importance of the validity, accuracy, sensitivity of the measures we use. I end with a reminder that methods must also be feasible.

Disseminating our findings is part of the scientific process, so that others know what we found. Not making our results available leads to duplication of effort because other researchers don’t know we did the work. Publication bias arises when researchers don’t publish findings because they are non-significant. We may need to publish to advance our career, but this is not the purpose of scientific articles. Confusing these two aims can lead to questionable research practices. This chapter goes through the of submitting a manuscript to a peer-reviewed journal. Peer review involves the scrutiny and evaluation of our work by experts. I begin with how to choose a journal, and things to consider before you submit, then I explain the cover letter, submission, and the review process. I explain the editor’s decision, what to do if your manuscript is rejected, revising your manuscript and resubmitting it. Finally, I cover what happens after your manuscript is accepted.

The ability to write is an essential component of research. We write to communicate with readers. Our readers include funding bodies, thesis examiners, manuscript editors, reviewers, or readers of a journal. In each case, we write to convince a reader of our argument. In reports, we also write to allow a reader to check and interpret our findings for themselves. Good writing conveys information to readers as clearly and simply as possible. Poor writing obscures meaning, frustrates the audience and puts them off reading our work. Poorly crafted writing can make the reader suspect that our science may also be confused. To avoid this, write clearly, simply, precisely and concisely. Writing takes practice. In this chapter I cover general points, which apply to all scientific writing. I begin with advice on drafting, and the need to revise, obtain feedback and revise your draft again. This iterative process can come as a surprise to students accustomed to submitting work for a deadline, then forgetting about it. I then cover general style, followed by specific topics including structure and clarity.

A clearly formulated research question is vital in science because it determines the data we need to collect, the methods we use, and, ultimately, the success of a project. Developing a research question is an iterative process of reading and thinking, as we define a problem and specify the contribution we hope to make to resolving it. This is not easy, and we learn through experience, and (if we’re lucky) from our mentors. In this chapter I first explain research questions and the case studies we use to address them, then look at where questions come from. I examine what makes a good research question and end with why reading is essential to the development of research ideas

Fieldwork can be exciting, and even addictive, but it can also be daunting and dangerous. Fieldsites range from a tent to established research stations. You may be close to home, or on the other side of the world. National researchers may be just as foreign to a local area as non-national researchers. You may be in a familiar environment or in a very unfamiliar one. Fieldwork often involves sharing living space with other people, and with wildlife. In this chapter I begin with what it takes to be a fieldworker, then cover permissions and logistics, field kit, personal safety, the social context, LGBTQIA+ concerns, natural hazards, physical health, mental health, and returning home.

Primates are an order of mammals which share a set of traits inherited from a common ancestor that distinguishes them from all other mammals. These derived traits are not all unique to primates and none of the individual traits is shown by all primates. Primates range in body mass from the 30 g Madame Berthe's mouse lemur to around 250 kg for a male Grauer's gorilla. This variation in size is in line with that found in other mammalian orders and is closely associated with what they eat (diet), how they move (locomotion), and their behaviour. In this chapter, I provide a general introduction to the primates and their evolutionary adaptations (traits produced by natural selection for their current function), including their distribution and habitats, adaptations to life in the trees, diet and dietary adaptations, brains and sensory traits, life history and reproduction, behaviour and locomotion, social behaviour and interactions with other species. I then survey the major groups of primates. Throughout the chapter, I highlight terms that are common in the literature but are problematic.

Data analysis and interpretation allow you to test your predictions and interpret your results. This is an exciting time and can be daunting because it’s a big change from data collection. It’s very unlikely that you will have collected exactly the data you set out to collect, but your analysis plan will keep you on track and avoid the dangers of aimlessly exploring your dataset. You will probably need further statistical advice at this stage. This chapter guides you through data preparation, initial data analysis, hypothesis testing, calculating your effect sizes and confidence intervals, interpreting your results and extrapolating from them.

Once you have a research question, hypotheses and predictions, study design, detailed methods and data analysis plan, you have all you need to write a research proposal. Writing a proposal clarifies your thoughts and ensures that they are feasible. You can share a proposal with other people to get feedback on your plans and use it to underpin funding applications. Whatever you propose to study, you are likely to need funding for equipment, supplies, transport, and other expenses. You may also need to cover tuition fees and living expenses. When you apply for funding you enter a competition. Most applications are unsuccessful. Some of the factors affecting success are out of your hands, such as the success rate, and luck. Some, however, are not, and excellent proposals share characteristics. In this chapter, I explain how you can improve your chances of obtaining funding by applying to appropriate organisations, tailoring your proposal carefully, following guidelines, and seeking feedback while preparing your application. I provide general points on writing and details of each section of a proposal. Finally, I address how to deal with the outcome of the funding decision. I focus on relatively small grants appropriate for PhD students and post-doctoral scholars. Much of the advice also applies to PhD project proposals.

To test predictions, we either observe natural variation and use statistical analysis to account for variation in covariates to estimate the influence of the variable we’re interested in, or we control variation in a planned manner, by manipulating one predictor variable and holding others constant. In this chapter, I review good study design and the strengths and weaknesses of observation and manipulation. I then explain that although it is easiest to describe observations and experiments separately, they lie at opposite ends of a continuum of researcher-imposed control on a study system.

We use statistical analyses to test our predictions using the measures we collect for our sample. Like all aspects of study design, we need to think carefully about our choice of analytical approach. Planning our data analysis in detail, before we collect your data, helps to determine what data we need to collect. It is very common to rush past the analysis plan and dive straight into collecting data. This is partly because statistics are not intuitive and can be intimidating. However, statistical analysis is an integral part of study design. We must understand statistics to understand the strengths, limitations, and potential biases of any research. This may seem daunting, but our understanding of statistics determines the quality of a study. The more we think about this now, the better our study will be. I begin this chapter with how to determine what sort of analyses we need and the need to consult a statistician when we design a study. Next, I cover problems associated with multiple testing and assessing multiple predictor variables. I explain how to prepare an analysis plan and suggest pre-registration.

Research integrity means conducting science in such a way that others can be confident in the methods we used and trust the findings we report. In addition to our responsibility to understand and comply with the ethical and legal obligations associated with our research, research integrity involves scrupulous honesty and the highest standards of rigour. However, a combination of our own biases, distorted career incentives, poor understanding of study design, and misuse of statistical analysis lead to practices that damage science (questionable research practices). Such practices undermine the validity of studies and increase the chance of erroneous results, leading to a literature based on false positive conclusions and studies that can’t be replicated. The inability to replicate the findings of published studies has been popularised as the replication crisis, particularly in medicine and psychology. In this chapter, I first define research misconduct and its consequences. I then review responsible practices and how to avoid questionable research practices. We’ll revisit these issues throughout the book.

It is our ethical duty to consider the possible consequences of our work and mitigate any risks, such that we avoid harm to the welfare and interests of our study animals, human participants, the environment, and the people we work with and alongside. We must also consider the effects of our research on our discipline and wider society. Reflecting on ethical dilemmas and weighing the positive and negative impacts of a project are essential to make informed decisions when planning a project and throughout a study. This can include the decision not to conduct a particular study, or to terminate it earlier than planned. In this chapter, I cover legal requirements and permits, then address the ethics of working with primates in captivity and the wild, specimen collection and working human participants. I then outline our ethical responsibilities to the natural environment, the people we work with, and the people we work alongside. I then highlight the importance of reflecting on our use of social media and the power of images, and end with our obligations to report and disseminate our findings.

A research project is not finished until we have written it up. Scientific reports have a standard format, with some variation. This should be familiar from your reading. This chapter builds on the general advice for writing in Chapter 14 and focusses on how to write a scientific report. I provide general guidance for writing your report, then cover each section of the manuscript in turn. I focus on primary research articles, because these are the main way in which we disseminate new research. Much of the advice applies more generally to theses and dissertations. Most reports have multiple authors and we must negotiate authorship fairly.

We may already be convinced of the value of studying primates, but we often need to convince others of that value in proposals, reports and papers. This chapter covers the reasons to study primates, including appreciation of their fascinating diversity and adaptations, their important ecological functions, their evolutionary relationship with humans, their socio-cultural importance, concern for their captive welfare, and their conservation status.

Statistical evidence is fundamental to science. Understanding statistics helps us to understand the literature and assess it critically, refine our research questions into testable hypotheses and predictions, design studies that are appropriate to test these predictions, evaluate whether our findings support our predictions, and derive appropriate conclusions. The dominant paradigm in primatology and allied disciplines is to test whether patterns we observe in our observations are due to more than random variation in our data. However, the statistical analyses we use to do this are very often misinterpreted. In this chapter I distinguish different kinds of variables, then introduce relationships between variables. I explain how we use statistical analysis to infer something about a theoretical population based on a sample. I introduce null hypothesis significance testing and explain common misunderstandings of this approach. I review the two types of error that arise in NHST and the concept of statistical power. I explain the need to assess and report effect sizes and confidence intervals, briefly introduce alternatives to null hypothesis statistical testing and end with how to interpret statistical results appropriately.

Like all science, studying primates is about asking the right questions in the right way. Most studies of primates fall within the life sciences, so I focus on the scientific method in this book. This chapter introduces how science works, then what it takes to be a primatologist. I outline the contents of the rest of the book and highlight the importance of keeping science healthy. I end by emphasising the need to respect other people and to promote inclusive science.