Statistics 1

elementary

K_W06

K_U06

A knowledge of statistics (as well as a knowledge of methodology and logic) is a basic element in a knowledge system of a person who studies any empirical scientific discipline – psychology in particular. The lectures in statistics are designed to present the process of how theoretical methodological knowledge is linked to statistical description and inference. This linkage is presented in particular research situations, in which a researcher deals with a wide range of data that should be described, analysed and interpreted.

Psychology students should understand a position of statistics in empirical sciences. They should be aware that psychology refers mainly to statistics, based on a convenient assumption of an infinite number of elements in each analysed population. This “idealized” approach might not be correct in other cases. In case of a finite population a way of constructing estimators is different.

The students should master the tools of statistical description, estimation and of statistical inference, which are designed to adequately describe and analyse empirical data and to draw correct conclusions on empirically tested hypotheses. The lectures introduce knowledge that is necessary to understand research procedures, to plan an empirical research and to interpret the results of appropriate statistical methods.

The students should master a basic as well as an advance knowledge on how empirical research is planned and on how empirical data (experimental and correlational) is analysed. At the same time, the students should be aware of any factors that may distort a validity of any empirical data.

The program of the first semester covers the basic statistical concepts, which are necessary to built any statistical description of analysed variables. The basic assumptions of statistical inference are also introduced.

Effects of teaching:

1. Knowledge: The students are able to define basic statistical concepts (a population, a sample, a random variable, probability). They know what is measurement in psychology and are able to describe various measurement scales.

Skills: The students are able to identify a sample and a population in a provided example of a research problem. They are able to specify the level of measurement of analysed variables; they can compare different measurement scales and different variables. The students know a difference between the concepts of fraction and probability.

Competences: The students are able to analyse the criteria for how the measurement level is defined for a given attribute and they are able to justify their position in this respect.

2. Knowledge: The students know the concept of a probability distribution and are able to recognize its characteristics. They know which estimator should be referred to in a given research situation. They can describe a transformation of standardization and characterize a normal distribution.

Skills: The students is able to create a distribution of the results (counts and fractions), obtained from the study of a n-element random sample. They are able to characterize the distribution, choosing correct statistical measures and are capable of drawing conclusions on the distribution of the variable at a population level.

Competences: The students synthesize all information on collected data, they are able to critically evaluate it and present the findings in a coherent way.

3. Knowledge: The students are able to explain a concept of an estimator, they know the difference between estimator and statistic. They are able to characterize probability distributions of basic estimators (mean, variance, Student's t statistic): they know their main characteristics, including degrees of freedom and their standard errors.

Skill: The students are able to graphically represent normal distributions and Student's t distribution (as well as their cumulative distribution functions). They are able to determine the range of values taken by the standard errors of statistics.

Competences: The students are aware of the meaning of the estimator and its distribution for a possibility to infer about the population characteristics.

4. Knowledge: The students are able to describe differences between estimation theory and statistical inference. They know concepts of: null hypothesis and alternative hypothesis, simple and composite. They are able to define errors in hypothesis testing. They can explain relations between errors, between errors and power of the test, and how errors are related to the sample size.

Skills: The students are able to construct a confidence interval for a population mean and variance. They are able to draw conclusions about the values of the population parameters considered. They are able to choose the correct test for a given null hypothesis (on a population mean), also accurately choosing between one-tailed and two-tailed tests.

Competences: In given examples of empirical problems, the students are able to make the correct decisions on a level of significance and a sample size.

ECTS:

Lectures - 30 hours

Practical classes - 30 hours

Consultations - 5 hours

Students’ preparations for the lectures - 25 hours

Students’ preparations for the practical classes – 35 hours

Students’ preparation for the assessment test – 55 hours

TOTAL – 180 hours [180 : 30 = 6]

ECTS points = 6