Statistics & Academic Writing

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Cursusdoel

Upon successful completion of this course, students will be able to:
  1. Design a statistically valid experiment to answer a given research question (hypothesis, sample size, controls etc);
  2. Use the Python modules scipy and statsmodels for basic statistical analysis techniques to a variety of biological problems;
  3. Select and apply the appropriate statistical analyses for a given dataset;
  4. Interpret the outcomes of a statistical analysis and draw the appropriate conclusions;
  5. Justify a reasoning about probabilities and probability distributions of a specific dataset;
  6. Understand the scientific paper structure and critically analyze scientific papers.
  7. Find reliable literature on a research question, and synthesize multiple papers into a mini narrative literature review, including a graphical abstract.
  8. Create a research graph and write a short results section based on this graph.
  9. Compose a writing plan and create a good overall structure, storyline, and argumentation.
  10. Write in a good academic style using correct English.
  11. Compose readable sentences of appropriate length and complexity, and paragraphs with good topic sentences and internal structure.
  12. Apply the principles of cohesion to their text.
  13. Self-reflect on their personal writing process and skills, create writing resolutions, and show personal growth as a writer.
  14. Give critical yet constructive feedback on the written work of peers.

Vakinhoudelijk

This course provides students with knowledge of basic statistical concepts and Python programming skills for analyzing data in the life sciences. Students also practice logical reasoning and academic writing skills as they analyze, draw conclusions from, and subsequently write up the results of their statistical analysis of a real data set.

Introduction:
A researcher must master a diverse set of skills to successfully complete the so-called research cycle that describes the sequence of activities involved in the generation of new knowledge. Two crucial skills are the ability to draw justified conclusions from data using statistics and the ability to report on a study and its outcomes in a well-readable and scientifically accepted format. Statistics are an integral aspect of scientific research, particularly for the life sciences which rely heavily on quantitative methodologies.

Set up of this course:
This course is divided into two parallel parts. On Tuesday’s Statistics and on Thursday’s academic writing

The first part of this course is set up around a weekly lecture followed by two practical sessions or tutorials. The three main areas of study are descriptive statistics, probability, and statistical inference. The strengths and limitations of statistical models to enable informed thinking are explored. Through context-rich practical exercises which are implemented in the widely used Python programming language for statistical computing and graphics, this course provides students with experience in the application of basic statistical analysis techniques to a variety of biological problems. Topics covered during this part of the course include: probability; reproducible research; data summarization; common distributions; hypothesis tests (and which [not] to use); linear models, regression, generalized linear models.

In the second part of the course, students will learn why academic is important and how academic writing can be a challenging but fun and fulfilling process. They will practice and demonstrate their writing skills through writing a mini narrative literature review and several smaller assignments. Giving and receiving peer feedback will be an important tool for becoming aware of and improving their own writing level.

Final assessment (please refer to the course manual):
Statistics exam (50%)
Academic writing part (50%)

The grade for the Statistics exam should be 5.5 or higher.

The Academic writing grade is further divided in the following elements that each need to be completed with either a pass or a grade of 5.5 or higher:

  • Active participation: pass/fail
  • Completing all ULearning assignments: pass/fail
  • Mini literature review (1500-1750 words): 70%
  • Research figure description (1/2 page): 10%
  • Peer review (based on 3 assignments): 10%
  • Self-reflection: 10%

Werkvormen

Hoorcollege

Werkcollege

Werkcollege 2

Toetsing

Eindresultaat

Verplicht | Weging 100% | ECTS 7,5

Ingangseisen en voorkennis

Ingangseisen

Er is geen informatie over verplichte ingangseisen bekend.

Voorkennis

Er is geen informatie over benodigde voorkennis bekend.

Voertalen

  • Engels

Cursusmomenten

Gerelateerde studies

Tentamens

Er is geen tentamenrooster beschikbaar voor deze cursus

Verplicht materiaal

Materiaal Omschrijving
BOEK Chapters 1-13 in Statistics for the Life Sciences, M.L. Samuels, J.A. Witmer and A.A. Schaffner, 5th Ed. 2015, Pearson Education Limited.

Aanbevolen materiaal

Er is geen informatie over de aanbevolen literatuur bekend

Coördinator

dr. ir. K. van Ommering k.vanommering@uu.nl

Docenten

dr. L. van Steijn l.vansteijn@uu.nl
dr. ir. K. van Ommering k.vanommering@uu.nl

Inschrijving

Let op: deze cursus is niet toegankelijk voor studenten van andere faculteiten, bijvakkers mogen zich dus niet inschrijven.

Inschrijving
Van maandag 18 september 2023 tot en met vrijdag 29 september 2023

Na-inschrijving
Van maandag 23 oktober 2023 tot en met dinsdag 24 oktober 2023

Inschrijving niet geopend
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