1. STATISTICS
An Introduction

2. Lecture #1 Topics Definition of Statistics Types of Statistics
Why Communication Science Students Learn Statistics
Population vs Sample
Levels of Measurement

3. SOME DEFINITIONS OF STATISTICS
The science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions (Lind, .....)
Facts shown in numbers (The Advanced Learner’s Dictionary of Current English, 1963)

4. TYPES OF STATISTICS
Descriptive Statistics: methods of organizing, summarizing, and presenting data in an informative way
Inferential Statistics/Inductive Statistics: the methods used to estimate a property of a population on the basis of a sample

5. DATA PRESENTATIONS Tables Bar Charts Pie Charts Line Charts
Scatter Plot Diagram
Histogram

6. TABLE

7. BAR CHART

8. PIE CHART

9. LINE CHART

10. SCATTER PLOT DIAGRAM

11. HISTOGRAM

12. Some Uses of Inferential Statistics
Parameter estimation, e.g.: 1) What is the average total time in a day spent by American teenagers to watch youtube channels? 2) What percentage of US adults between the ages are on Facebook?
Hypothesis testing, e.g.: 1) Is there any correlation between people education level and their favoured news topics? 2) Was last week debate between two candidates in the 2019 Presidential Election effective in changing viewers’ preferences for the candidates?

13. Why must students majoring in Communication Science study statistics?
Statistics roles as a quantitative method in researches on communication sciences, e.g.: “Do 3 C’s (Conflicts, Corruptions, Crimes) reportings on television and internet have influence on the sense of patriotism of Indonesian children?”
In journalistic: Statistics teach us how to present data in a proper and informative way

14. INCORRECT vs CORRECT DATA PRESENTATION (1)

15. INCORRECT vs CORRECT DATA PRESENTATION (2)

16. POPULATION vs SAMPLE
Population: (1) the entire set of individuals or objects of interest, (2) the entire set of measurements obtained from all individuals or objects of interest
Sample: a portion, or part, of the population of interest

17. DATA CLASSIFICATION according to LEVELS OF MEASUREMENT
Nominal
Ordinal
Interval
Ratio

18. PROPERTIES OF NOMINAL LEVEL DATA
The variable of interest is divided into categories or outcomes.
There is no natural order to the outcomes.

19. PROPERTIES OF ORDINAL LEVEL DATA
Data classifications are represented by sets of labels or names that have relative values.
The data classified can be ranked or ordered.

20. PROPERTIES OF INTERVAL LEVEL DATA
Data classifications are ordered according to the amount of the characteristic they possess.
Equal differences in the characteristic are represented by equal differences in the measurements.
Zero is relative

21. PROPERTIES OF RATIO LEVEL DATA
Data classifications are ordered according to the amount of the characteristic they possess.
Equal differences in the characteristic are represented by equal differences in the measurements.
The zero point is the absence of the characteristic and the ratio between two numbers is meaningful (Zero is absolute)

22. HOW TO DETERMINE DATA LEVEL