Statistics involves collecting, organizing, analyzing, and interpreting data. Descriptive statistics describe characteristics of a data set through measures like central tendency and variability. Inferential statistics draw conclusions about a population based on a sample. Key terms include population, sample, parameter, statistic, data types, levels of measurement, and sampling techniques like simple random sampling. Common data gathering methods are interviews, questionnaires, and registration records. Data can be presented textually, in tables, or graphically through charts, graphs, and maps.

1. Statistics

16. Determining the Sample Size Slovin’s Formula: n is the sample size N is the population size e is the margin of error The margin of error is a value which quantifies possible sampling errors.

17. Determining the Sample Size The margin of error can be interpreted by the use of ideas from the laws of probability. In reality, it is what statisticians call a confidence interval. Sampling error means that the results in the sample differ from those of the target population because of the “luck of the draw”.

21. Sampling Techniques 2. Systematic Sampling: Samples are chosen following certain rules set by the researchers. This involves choosing the k th member of the population, with k=N/n, but there should be a random start.

22. Sampling Techniques 3. Cluster Sampling: is sometimes called area sampling because it is usually applied when the population is large. In this technique, groups or clusters instead of individuals are randomly chosen.

23. Sampling Techniques 4. Stratified Random Sampling: This method is used when the population is too big to handle, thus dividing N into subgroups, called strata , is necessary. A process that can be used is proportional allocation .

24. Sampling Techniques B. Non Probability Sampling: Each member of the population does not have a known chance of being included in the sample. Instead, personal judgment plays a very important role in the selection. Non-probability sampling is one of the sources of errors in research.

26. Sampling Techniques 3. Purposive Sampling: Choosing the respondents on the basis of pre-determined criteria set by the researcher.

30. Data Gathering Techniques The Questionnaire (characteristics) 2. There is a descriptive title/name for the questionnaire. 3. It is designed to achieve objectives. 4. The directions are clear 5. It is designed for easy tabulation.

31. Data Gathering Techniques The Questionnaire (characteristics) 6. It avoids the use of double negatives. 7. It also avoids double barreled questions. 8. It phrases questions well for all respondents.

34. Data Gathering Techniques 3.The Registration Method: This method of gathering data is governed by laws. A: Most reliable source of data D: Data are limited to what are listed in the documents

35. Data Gathering Techniques 4. The Experimental Method: This method of gathering data is used to find out cause and effect relationships. A: Can go beyond plain description D: Lots of threats to internal and external validity

36. Presentation of Data Textual Form: Data are presented in paragraph or in sentences. This includes enumeration of important characteristics, emphasizing the most significant features and highlighting the most striking attributes of the set of data.

37. Presentation of Data Tabular Form: A more effective device of presenting data. 1. stem and leaf plots 2. frequency distribution table 3. contingency table

38. Presentation of Data Graphical/Pictorial Form: A most effective device of presenting data. 1. line graph (freq. polygon, ogive) 2. bar graph (histogram) 3. pie chart 4. pictograph 5. statistical maps