This document discusses sampling methods and statistics. It describes population, sample, and target population. There are four basic reasons for using samples: greater speed, reduced cost, greater accuracy, and greater scope. Two main types of sampling methods are described: probability sampling, which uses random selection, and non-probability sampling, which has no system for selection. Several specific probability and non-probability sampling techniques are defined. The document also covers sampling error, standard error of the mean, confidence intervals, and confidence levels. It provides examples of how to construct confidence intervals.

1. Prepared by:
Walter Phillip SP. Palad, R.N.
STATISTICS

2. SAMPLING METHODS
 Sample
 Population
 Target population

3. 4 BASIC REASONS FOR THE
USE OF SAMPLES
greater speed.
reduce cost.
greater accuracy.
greater scope.

4. TYPES OF SAMPLING
METHODS:
PROBABILITY SAMPLING
 use some form of random selection
 equal probabilities of being selected
 there is an “OBJECTIVE” way of assessing reliability of result.
NON PROBABILITY SAMPLING
 the sample is not a proportion of the population and there is
no system in selecting sample.
 the selection depends on the situation

5. PROBABILITY
SAMPLING
 Pure/ Simple Random Sampling
( Lottery sampling or Fishbowl
Method )
 equal chance of being selected.
 Systematic Sampling ( Restricted
Random Sampling )
 alphabetical arrangement,
residential or house arrays,
geographical placement, etc.
 e.g. 20% of sample size. If 100%
is divided by 20%, then the result
is 5, so every 5th name will be
taken from the population.

6.  Stratified Random Sampling
 grouped in to a more or less homogenous classes
 CLASSIFICATION: Horizontal and Vertical
 Horizontal: BSED, BSN, BSHRM at same year
 Vertical: 1st year, 2nd year, 3rd year and 4th year or Age 10,
11, 12, etc.
 Cluster Sampling ( Area Sampling )
 heterogonous individual
 used when population is very large and wide (community)

7. NON PROBABILITY SAMPLING
 the sample is not a proportion of the
population and there is no system in
selecting sample.
 the selection depends on the situation

8.  Purposive Sampling
 Convenience Sampling
 Quota Sampling
 Accidental Sampling

9. SAMPLING ERROR
 “ chance differences ”
 Taking larger sample sizes can reduce sampling error,
although this will increase the cost of conducting
survey.

10. STANDARD ERROR OF THE
MEAN
Note: Standard deviation is computed by getting the square
root of the variance.
Note: To compute for standard error of the mean, divide the
computed standard deviation by the square root of the total
( sum ) frequencies

11. CONFIDENCE INTERVALS AND
CONFIDENCE LEVELS
Statisticians use a confidence interval to describe the
amount of uncertainty associated with a sample
estimate of a population parameter.
The confidence level describes the uncertainty
associated with a sampling method.

12. EXAMPLE
1. A sample of 16 students is taken. The
average age in the sample was 22 years with
a standard deviation of 6 years. Construct a
95 % confidence interval for the average age
of the population.

13. 2. A sample of 100 bean cans showed an
average weight of 13 ounces with a standard
deviation of 0.8 ounces. Construct a 90%
confidence interval for the mean of the
population.

14. PRACTICE SET:
1. Construct a 90% confidence for the population
mean, m. Assume the population has a normal
distribution. In a recent study of 22 eighth graders,
the mean number of hours per week that they
watched television was 19.6 with a standard
deviation of 5.8 hours.

15. 2. A random sample of 40 students has a
mean annual earnings of $3120 and a
standard deviation of $677. Construct the
90% confidence interval for the population.