Research Hypotheses are temporary answers to research questions. Hypotheses can be explained from various points of view, for example etymologically, technically, statistically, and so on. Generally, the widely used notion is that a hypothesis is an answer while researching. Well, we will discuss more deeply and provide examples of these hypotheses.

What is meant by Population and Sample? In simple terms, the population is the entire research subject, while the sample is part of the population. Let us discuss in this article in detail and clearly the population and sample and the differences between them. The difference in population and sample must be clearly understood so that it is not wrong when researchers conduct research. It is therefore important to understand the population and sample in the context of the Research Methodology.

The definition of population is:

What is the Population? The population is the total number of units or individuals whose characteristics are to be studied. And these units are called units of analysis and can be people, institutions, things, etc. (Djarwanto, 1994: 420).

Understanding Population and Sample According to Experts

16 Definition of Population According to Popular Experts

The following is an understanding of the population according to experts.
Definition of population according to experts: 

1. Netra, Nawawi and Arikunto:
-According to Netra (1976), population is all individuals who are general or general who have characteristics that tend to be the same.
-According to Hadari Nawawi (1983), Population is the whole object of research consisting of humans, animals, objects, growth, events, symptoms, or test scores as a source of data that has certain characteristics in a study conducted.
-According to Arikunto Suharsimi (1998: 117), population is the whole object of research. If someone wants to examine an element that is in the research area, then the research is a population study.

2. Definition of population according to experts: Sugiyono, Bugin and Nursalam:
-According to Sugiyono (1997: 57), population is a generalization area consisting of objects / subjects that have certain quantities and characteristics that are determined by the researcher to be studied and then draw conclusions.
-According to Bugin (2000: 40), population is the whole (universe) of research objects in the form of humans, animals, plants, air, symptoms, values, events, life attitudes, and so on so that this object can be a source of research data.
-According to Nursalam (2003), population is the whole of the variables concerning the problem under study.

3. Understanding Population According to Experts: Furchan, Margono, Nazir, Sabar and Zuriah:
-According to Furchan (2004), population is an object, all members of a group of people, organizations, or groups that have been clearly defined by the researcher.
-According to Margono (2004), population is all data that is the center of attention of a researcher within a predetermined scope and time. Population is related to data, if a human being provides data, then the size or number of the population will be as many humans.
-According to Nazir (2005), population is a group of individuals with qualities and characters that have been determined by the researcher. Traits, characteristics, and qualities are known as variables. He divides the population into two, namely finite and infinite populations.
-According to Sabar (2007), population is the entire object of research. If someone wants to research all the elements in the research area, then the research is a population study or population study or census study.
-According to Zuriah (2009: 116), population is all data that concerns researchers within a predetermined scope and time.

4. Understanding Population According to Experts: Sudjana, Widyanto, Mulyatiningsih, Howell and Morissan:
-According to Sudjana (2010: 6), Population is the totality of all possible values, counting or measurement results, quantitative or qualitative regarding certain characteristics of all complete and clear group members who want to study their properties.
-According to Widiyanto (2010: 5), population is a group or collection of objects or objects that will be generalized from the research results.
-According to Mulyatiningsih (2011: 19), population is a group of people, animals, plants, or objects that have certain characteristics to be studied. The population will be the area for generalizing the conclusions of the research results.
-According to Howel (2011: 7), population is a collection and events where you are interested in these events.
-According to Morissan (2012: 19), population is a collection of subjects, variables, concepts, or phenomena. We can examine each member of the population to find out the nature of the population concerned.

Thus above are some of the definitions of the population according to experts.

Definition of Samples

What is a sample according to the experts? The following is the understanding of the sample according to the experts:

3 Definition of Samples According to Experts

According to Sugiyono (2008: 118), the sample is a part of the whole as well as the characteristics of a population.

If the population is large, so the researchers certainly do not allow it to study the entirety of that population because of several obstacles that will be faced later such as limited funds, energy, and time. So in this case it is necessary to use samples taken from that population.

And then, what is learned from the sample will get the conclusion that will be applied to the population. Therefore, the sample obtained from the population must be truly representative (representing).

According to Arikunto (2006: 131), the sample is a part or representative of the population to be studied. If the research is carried out by part of the population, it can be said that the research is a sample study.

According to Nana Sudjana and Ibrahim (2004: 85), the sample is part of the population that can be reached and has the same characteristics as the population that the sample is taken from.

Thus above are some of the understanding of the sample according to the experts. After reading the explanation above, hopefully the readers have understood or at least have a picture of the meaning and differences of the population and sample.

Summary of Population and Sample Differences

Let's summarize the differences between the population and the sample based on an illustration as follows:

If the population is considered too large, in order to save time, cost, and energy, the researcher does not examine all members of the population but will use a sample.

When the researcher intends to study only part of the population (sample), the question that always arises is how many samples meet the requirements. There is a statistical law in determining the number of samples, namely the greater the number of samples, the more it describes the state of the population (Sukardi, 2004: 55).

Determination of the number of samples based on population characteristics

And besides, based on the provisions above, it is also necessary to determine the number of samples studied from the characteristics of the population. If the population is homogeneous, a large sample is not required. For example, in checking blood type.

Although the use of large sample size is highly recommended, considering the various limitations of the researcher, the researcher tries to take a minimum sample with the statistical requirements and rules being met as recommended by Isaac and Michael (Sukardi, 2004: 55).

By using a certain formula (see Sukardi, 2004: 55-56), Isaac and Michael give the final result of the number of samples to the total population between 10 - 100,000.

Definition of Data 

Data is any information that has been collected, observed, generated or made to validate research findings that contain originality (Sekaran, 2016). Even data is the raw material that forms all research reports (Dempsey & Dempsey, 2002: 76). Based on the explanation of the experts above, in this article, the writer will mention the term data as research data.

Understanding data in a broad sense is a collection of information that can be amplified, processed, transmitted and analyzed. However, if we want to interpret data in a narrow sense in the context of research, then what is meant by data is research data. For the second meaning, it is better if we refer to the research definition data that has been put forward by the experts above.

Classification of Data

Research data can be classified based on the nature, source, and also the scale of measurement. Below we will explain one by one about the classification of research data:

Based on the nature:

1) Quantitative data: data in the form of numbers. For example, weight, house area, height, IQ score, etc.

2) Qualitative data: data in the form of words or statements. Can also be interpreted as categorical data, because it is usually in the form of categories or groupings based on certain names or initials. For example: Groups of Civil Servants, Farmers, Laborers, Entrepreneurs, etc.

Data Based on the source

Based on the source, the data are classified as follows:

1. Primary data

Primary data is data obtained directly by the party for which the data is required.

2. Secondary data

Secondary data is data that is not obtained directly from the party for which data is required.

Data Based on the Measurement Scale

Based on the measurement scale, the data are classified as follows:

Data which is the measurement result of research variables, has the type of measurement scale as contained in the research variables. Thus, based on this review, the data can be divided into:

1. Nominal Data

Nominal data is a type of qualitative data, in which there is a category in which there is no difference between higher and lower degrees. For example: The sex of women and men, where men are not necessarily higher than women, and vice versa.

2. Ordinal Data

Ordinal data is almost the same as nominal data, only there are differences in degrees higher and lower. For example: Education, where tertiary education is higher than SMA, and vice versa, SMA education is lower than tertiary education.

3. Interval data

Interval data is data that belongs to the quantitative data group, which is in the form of numbers in which mathematical operations can be performed and the order between one data and another has the same range. For example: Test scores, which are said to be sequential with the same range, namely after 1 then 2 then 3 and so on. And it is said that mathematical operations can be performed, for example: the number 1 can be multiplied by the number 2 and the result is 2.

Another important characteristic is that the interval data does not have the absolute 0 and 100 absolute at the same time or in another sense the percentage between one data and the whole data cannot be ascertained. means absolute 0, for example, the test score. In common sense, there cannot be a test score less than 0. Whereas 100 is absolute for example test scores, logically there cannot be an exam score of more than 100.So the interval data is for example body weight, where it cannot be ascertained how much the highest score actually is weight body. It could be that people weigh tens of kilos, hundreds or even thousands of kilos.

4. Ratio Data 

Ratio data is data that is actually the same as iterval data, but the difference is that ratio data can be made a percentage because there are absolute 0 and 100 values. As discussed above, for example test scores that have a value limit of 0 to 100.If a student gets a score of 25, it can be interpreted that the score is 25% of the maximum value of 100.