a PowerPoint about research analysis on the diversity of a certain organisms in a specific place and their abundance and environmental factors that could possibly affect their existence in the area this document also includes the presentation of my group and a comprehensive analysis on lichen life in the baranggay unfortunately it's not the final research for this paper so all the details are not yet to include tho alot of important information were included so that a general understanding of he topic is expected to be explained very well including all the important details

2. SAMPLING
 Sampling is a research method that selects a
specific segment from the population at the
center of study, focusing on common
characteristics that interest the researcher and
provide insights into the population's
characteristics.

3. Two groups or population
 Target population is a composed of the entire Group of people or objects
to which the researcher wishes to generalize the findings of the study
 Accessible population is the portion of the population to which the
researcher has responsible access.
 Subjects are individuals or entitiles which serves as the focus of the study.
 Respondents are individuals or a group of people who actively serve as
source of information during data collection Elements refer to subjects of
the study who are not people

4. WHAT IS STATISTICS?
 Statistics is a field that focuses on developing and studying
methods for collecting, analyzing, interpreting, and
presenting impirical data. It is an interdisciplinary field that
applies to various scientific fields and research questions.
Statistics uses quantified models, representations, and
synopsis for experimental data or real-life studies. It is used
to gather reviews, analyze, and draw conclusions from data.

5. WHAT IS A PARAMETER?
 A constant or variable term In a function that
determines The specific form Of a function but not
it's general nature A variable entering into the
mathematical form of any distribution such that
the possible Volume of the variable correspond to
different distributions

6. What is the difference between a statistic and a
parameter?
 A statistic and a parameter are very similar.
They are both descriptions of a group. Like
"50% of dog owners prefer x brand dog food"
The difference between a statistic and a
parameter is that statistics Describe a sample.
A parameter described an entire population

7. Factors and determining sample
size

8. Factories to consider in the termining the
sample size
 1. Homogeneity Of the population , the higher the degree of the
homoginiety of the population , the smaller the sample size that can
be your utilize
 2.Degree of precision Decide by the researcher. The larger the sample
size the higher the precision or accuracy of the results will be
 3.Types of the sampling procedure probability simpling uses sample
size that none probability sampling

9. Various approaches to determining the sample size
 1.Sample size as small as 30 as generally adequate But ensure that something
distribution of the mean will approximate the normal curve
 2.When the total population is equal to or less than 100 this, some number may serve
as the sample size. These called universal sampling
 3.Slovins formula Is your stock computed for sample size(Sevilla,2003)
 4.Acording to Gay 1976, the following are acceptable sizes for different types of
research.
 A.DESCRIPTIVE RESEARCH- 10% to 20% may be required
 B. COMPARATIVE RESEARCH- 15 subjects or groups

10. The theory of sampling

11.  1. Researchers want to gather information about a whole
group of people (the population).
 2. Researchers can only observe a part of the population (the
sample).
 3. The findings from the sample are generalized, or extended,
back to the populationTherefore, the key question in sampling
is How representative is the sample of the target population?
This question is the foundation of population validity, the
degree to which the results of a study can be generalized from
the sample to the target population.

12.  The analogy of a fruit market can be used when thinking about the
population. the sample, and the sampling technique. The first step
in sampling is to identify the unit of analysis. Let's say that you are
conducting research related to a fruit market. What will be studied
in the fruit market? Is it a type of fruit or the fruit sellers
themselves? Let's say you identify citrus fruit as the unit of analysis,
and your popullation is ali citrus fruit within the Bauchi Road fruit
market. There are too many pieces of citrus fruit for you to study in
that market, so you must select only a sample of the citrus fruit.

13.  A common error in sampling is that the sample and
population are not identical. For example, the sample
may be too narrow, if the population is all citrus fruit
within the Bauchi Road last market, then the sample
cannot only consist of lemons because your sample
would be missing oranges, grapefruit, and limes.
Therefore, you must find a way of selecting a
representative sample of citrus fruit from your
population. To apply to an educalional study, perhaps
one may say that the population is all students, but
only university students in public schools are sampled

14.  Another common error is to make the population too
broad. Some may say that the population is all mangoes in
the Bauchi Road fruit market, but they are really only
interested in green mangoes. If only green mangoes are of
interest, than the population should be green mangoes in
the Bauchi Read fruit market in educational research,
sometimes researchers are only interested in a population
with a certain characteristic, such as student who has not
chosen a career (in the case of career Thus, the population
and sample must be the same.

15. Preliminary Considerations in Selecting a
Sample
 To select a sampling procedure, first choose the unit of analysis, such as
students, and determine the number of units needed. Larger samples are
more representative and provide stronger statistical power, but they can
decrease the quality of research studies, especially for experimental and
quasi-experimental designs. Overpopulation in classrooms and experiments
can reduce the impact of treatment, so smaller treatment groups are
generally preferable. Descriptive designs require at least 100 participants,
correlational designs require 30 participants, and experimentat, quasi-
experimental, and causal-comparative designs require at least 15
participants per group. The size of the sample in experiments depends on
the effectiveness of the treatment, with a large sample size needed for weak
treatments or weak treatments.

16. SIMPLE RANDOM SAMPLING
 In simple random sampling, every individual in the target
population has an equal chance of being part of the
sample. This requires two steps.
 1.Obtain a complete list of the population.
 2. Randomly select individuals from that list for the
sample.

17.  In educational studies, simple random sampling is rarely
used due to the requirement for a complete list of
students in the target population. Random, a technical
term in social science research, means selection made
without aim, reason, or patterns. Researchers use specific
procedures like the hat-and-draw method or a random
number table to ensure random selection. Participants
cannot be chosen based on factors like intelligence,
gender, social class, or convenience. Using the term
"random" when the unit of analysis was not selected using
these methods is either irresponsible or untruthful.

18. STRATIFIED RANDOM SAMPLING
 In stratified random sampling, the researcher first divides the population
into groups based on a relevant characteristics and then selects
participants within those groups. In educational research, stratified
random sampling is typically used when the researcher wants to ensure
that specific subgroups of people are adiequately represented within the
sample. For example, a research study examining the affect of
computerized instruction on math's achievement needs to adiequately
sample both male and female pupils stratified random sampling will be
used to ensure adequate representation of both males and females.
Stratified random sampling requires four steps:.

19.  1. To conduct a population analysis, the researcher must
first identify the strata, which are the characteristics of the
population, such as gender, age, and urban or rural areas.
The number of participants for each stratum should be
determined, either by using proportionate random
sampling or by dividing the unit of analysis into the
respective strata. For example, if the target population is
students, the researcher should list one list of male
students and another list of female students. Then,
participants should be randomly sampled from within the
group using methods like hat-and-draw or random
number tables.

20. Purposive Sampling
 In purposive sampling, the researcher used their expert
judgement to select participants that are representative
of the population. To do this, the researcher should
consider factors that might influence the population,
perhaps socio -ecnomic status, intelligence, access to
education, etc. Then the researcher purposefully select
a sample that adequately represent the target
population on these variables.

21. Multi-Stage Sampling
 Educational researchers often use multi-stage
sampling, selecting a sample in multiple stages. The
first stage selects geographical regions, the second
stage selects schools, and the third stage selects the
unit of analysis, such as teachers or students. Other
sampling techniques may be used at different stages,
such as random sampling, purposive sampling, or
stratified sampling.

22. The steps of multi-stage sampling are
as follows:
 • Organize the sampling process into stages where
the unit of analysis systematically grouped.
 • Select a sampling technique for each stage.
 • Systematically apply the sampling to each stage
until the unit analysis has been selected.

23. How to choose your sampling strategy to
guarantee relevant results.
 To ensure data representativeness, choose a sampling strategy
during the data journey. This step ensures reliable and reflects
the characteristics of the target group. This blog will outline
the process of collecting primary data, using an example of a
survey on residents in five towns with 3,200 households, as the
target population. This will help in understanding the sampling
strategy and its importance in data collection.

24. Step 1: Define your sample and target
population.
 all times your survey may require you to cover the entire
target population as is the case in mapping or population
studies. that's usually reffered to as a census survey, however,
target populations are generally large and expensive to survey
in our example, it may not be feasible to visit all 3,200
households of the fie towns instead, you'd wants to choose a
sample that would be representative of the population and
reflect its characteristics.

25. step 2 define your sample size
 the first step in your sampling exercise will be to decide on an
appropriate sample size. there are no strict rules for selecting a
sample size. you can make a decision based on the objectives of
the project, time available, budget, and the necessary degree of
precision.in order to select the appropriate sample size, you will
need to determine the degree of accuracy that you want to
achieve for this ,you'll need to establish the confidence interval
and confidence level of your sample.

26. Step three: Define your sampling technique
 Once youve chosen the sample size for you sure
yo the need to define which. Sampling technique
you'll use to select your sample from the target
population. The sampling technique that's right
for you depends on the nature and objectives of
your project. Sampling techniques can be broady
divided into two types: random samoling) and
non-random sampling.

27. Random sampling
 Random sampling involves selecting a sample randomly from a population without
specific conditions, either through a list or physically at the survey point. Simple
random sampling without replacement ensures that a particular household is not
selected multiple times, while systematic sampling divides the total population by
the sample size to arrive at a sampling interval. Systematic sampling works best
when the population is homogeneous, with most people sharing the same
characteristics. In a mixed and heterogeneous population, stratified random
sampling is used to ensure sufficient inclusion of all categories. This involves
calculating the proportion of each strata within the population and selecting the
sample in the same proportion, either randomly or systematically. For example, in a
sample of five towns, the proportion of each town within the sample size of 345 is
calculated, and the sample size (345) is proportional and cyclic across the towns.

28. Non -random sampling
 Non-random sampling is a method used in studies where a
sample is collected based on specific characteristics of the
population. It includes methods like convenience, judgment,
quota, and snowball sampling. It is important to minimize
sampling error to ensure the chosen sample is representative of
the population. The extent of errors depends on the sampling
technique chosen.

29. Step four: Minimize sampling error
 To minimize sampling error in a sample selection study, it is crucial
to ensure that the chosen sample is representative of the
population. The robustness of the sample depends on how much
sampling error is minimized. Random sampling techniques allow
for a level of error regulation, while non-random sampling
techniques leave the sampling error unknown. For instance, if the
survey needs to infer the proportion of certain characteristics of
the target population, random sampling can be used. However, for
situations where sampling errors or proportionality are not a
concern, non-random sampling techniques can be used. Without a
sampling strategy, biased or non-representative data may be
collected, rendering the data invalid.

30. THANK
YOU!