*Conducted in terms:*2019/20_Z, 2020/21_Z, 2021/22_Z

*Erasmus code:*13.1

*ECTS credits:*unknown

*Language:*Polish

*Organized by:*Faculty of Biology and Environmental Sciences

*Related to study programmes:*

# Statistical methods in biology I WB-BI-41-01

he aim of the course is to familiarize students with the basic and commonly used statistical analyzes in biological sciences. Students will work in the Statistica and SPSS statistical programs.

Course participants will learn about the following thematic blocks:

1. getting to know the functioning of Excel, Statistica, SPSS, creating databases, importing data, sorting data, creating subsets, creating a random sample, removing missing data, formulating a research question, forming zero and alternative hypotheses, errors of type I and II, types of variables, measuring scales

2. measures of central tendency, measures of variability and dispersion, mean, median; kurtosis, skewness; measures of variation: standard deviation, variance, percentiles, quartiles, 95% confidence interval, interquartile range

3. statistical analysis of data: normal distribution, assessment methods, multi-division tables, distribution series, parametric tests: t for dependent and independent groups, charts for t tests, nonparametric comparison of two groups (Mann-Whitney U test, Wilcoxon test), analysis variance (parametric methods), post-hoc tests, nonparametric equivalents of the analysis of variance (ANOVA Kruskall-Wallis rank test and median test; Friedman test; W Cochran test), correlation and linear regression analysis

Term 2019/20_Z:
The aim of the course is to familiarize students with the basic and commonly used statistical analyzes in biological sciences. Students will work in the Statistica and SPSS statistical programs. |
Term 2020/21_Z:
The aim of the course is to familiarize students with the basic and commonly used statistical analyzes in biological sciences. Students will work in the Statistica and SPSS statistical programs. |
Term 2021/22_Z:
None |

## (in Polish) E-Learning

## (in Polish) Grupa przedmiotów ogólnouczenianych

## Subject level

## Learning outcome code/codes

## Type of subject

## Course coordinators

Term 2020/21_Z: | Term 2019/20_Z: | Term 2018/19_Z: | Term 2021/22_Z: |

## Learning outcomes

Subject effects in the field of knowledge:

Objective effect 1 - the student knows and understands the legitimacy of using empirical data in the interpretation of biological phenomena and processes,

Subject effect 2 - the student knows and understands the legitimacy of using an appropriate statistical method, understands and is able to present the validity of the selection of an appropriate statistical method, is able to interpret the obtained results

Skills subject effects:

Subject effect 3 - the student is able to use his theoretical knowledge and implement it to collect, analyze and interpret empirical data, and can also use statistical programs for this purpose

Subject effects in the field of social competences:

Subject effect 4 - the student is ready to critically evaluate his knowledge and systematically update his knowledge of statistics and its practical applications

## Assessment criteria

2 partial tests and final test; open-ended questions (students will formulate research questions, hypotheses, and then perform subsequent tasks in the statistical program. Assessment criteria:

2 partial tests and final test; open questions (students will formulate research questions, hypotheses, then perform subsequent tasks in the statistical program and provide the obtained result along with its interpretation): the correctness and clarity of the research questions and hypotheses will be scored, correctness of the obtained result and correctness of its interpretation

2. carrying out a research project, based on the available databases, students will perform a statistical analysis of the obtained data; the type of research questions asked, the method of performing the statistical analysis (correctness of formulating hypotheses, correctness of the selection of the statistical test and justification of the choice), correctness of the interpretation of the results will be assessed

The final grade will be the weighted average of the grades obtained from the partial tests (weight 1), the final test (weight 2) and the performed statistical analysis (weight 1). A student may have 1 unexcused absence.

Scoring tests and final project:

100-94% - 5

93-88% - 4.5

87-80 - 4

79-70% - 3.5

69-60% - 3

59 and less - 2

Knowledge:

for the grade 2 (Ndst.): the student does not understand the need to use empirical data in the interpretation of biological phenomena and processes, the student does not know and does not understand the validity of the use of an appropriate statistical method in the interpretation of empirical data, cannot choose an appropriate statistical method, cannot interpret the results obtained

to grade 3 (dst): the student understands the need to use empirical data in the interpretation of biological phenomena and processes, but understands to a limited extent the legitimacy of using an appropriate statistical method in the interpretation of empirical data, the choice of an appropriate statistical method is not clear to him, he is poorly informed about the method interpreting the results

to grade 4 (db): the student understands the need to use empirical data in the interpretation of biological phenomena and processes, knows and understands the legitimacy of using an appropriate statistical method in the interpretation of empirical data, is able to choose an appropriate statistical method, is familiar with the method of interpreting the results

to grade 5 (very good): the student understands the need to use empirical data in the interpretation of biological phenomena and processes, is very well versed in statistical methods and is able to make an appropriate choice of the method depending on the analyzed data (can justify his choice), he is very good at interpretation of the obtained results

Skills

for the grade 2 (ndst.): the student is not able to use theoretical knowledge on basic statistical methods and implement it to collect, analyze and interpret empirical data, and cannot use statistical programs for this purpose

to grade 3 (dst): the student can use theoretical knowledge on basic statistical methods to a limited extent and implement it to collect, analyze and interpret empirical data, can, although not fluently, use statistical programs for this purpose

to grade 4 (db): the student is able to use theoretical knowledge on basic statistical methods and implement it to collect, analyze and interpret empirical data, can use statistical programs

to grade 5 (very good): the student very well uses the theoretical knowledge of basic statistical methods and uses it very well to collect, analyze and interpret empirical data, very well uses statistical programs for this purpose

Social competence:

For the grade 2 (ndst): the student is not able to critically assess his knowledge and systematically update the knowledge in the field of statistics and its practical applications

For the grade 3 (dst): the student has a limited ability to critically evaluate his knowledge, and it is difficult for him to update it and understand the practical applications of statistics

To grade 4 (db): the student is able to make a critical assessment of his knowledge of statistics, he is good at updating and extending it, he is good at using it in practice

To grade 5 (very good): the student is very good at critically assessing his knowledge in the field of statistics, he is very good at updating it, is very good at using it in practice

## Practical placement

not applicable

## Bibliography

Recommended literature:

Literatura obowiązkowa:

Jóżwiak J, Podgórski J. 2012. Statystyka od podstaw. PWE Polskie Wydawnictwo Ekonomiczne

Stanisz A, 2000 - Zbiór artykułów w czasopiśmie Medycyna Praktyczna 2000/04. Podstawy statystyki dla prowadzących badania naukowe; np:

https://www.mp.pl/artykuly/10895,pomiary-powtarzane

https://www.mp.pl/artykuly/13389,abc-raportu-statystycznego https://www.mp.pl/artykuly/10851,analiza-wariancji-testy-po-fakcie

Acdditional lierature:

Logan M. 2010. Biostatistical design and analysis using R. A practical guide. Wiley-Blackwell, Chichester.

Stanisz A. 2007. Przystępny kurs statystyki. T.1,2. StatSoft, Kraków.

Quinn G.P, Keough M.J. 2011. Experimental design and data analysis for biologists. Cambridge University Press, Cambridge.

Term 2021/22_Z:
None |

## Notes

Term 2021/22_Z:
None |

## Additional information

Information on *level* of this course, *year of study* and semester when the course
unit is delivered, types and amount of *class hours* - can be found in course structure
diagrams of apropriate study programmes. This course is related to
the following study programmes:

Additional information (*registration* calendar, class conductors,
*localization and schedules* of classes), might be available in the USOSweb system: