Observing
Observation for science is a method used to gather information by carefully examining a phenomenon, event or natural process. Observation is a step that forms the basis of scientific research and contributes to the development of scientific knowledge.
While observing, the observer carefully watches the event or phenomenon, records the data and identifies various details, behaviors, characteristics or relationships. Observation can be carried out in the natural environment as well as in the laboratory environment. The purpose of observation is to collect, verify or not verify data to develop scientific explanations and theories about the reality of the natural world.
Observations made for science must be objective and carried out impartially. The observer should avoid biases or prejudices and record data objectively. Also, observations must be repeatable and measurable so that others can arrive at the same conclusions or reanalyze the data.
Observation is part of the scientific method and is used in conjunction with other steps such as formulating hypotheses, planning experiments, and analyzing data. Observation is an essential tool for scientists to understand and explore the natural world, and helps uncover new information.
Basic concepts and techniques related to observation
Natural Observation: Observing the event or phenomenon in the natural environment, in real conditions. The data obtained in this way reflect the true state of the phenomenon. For example, observing the behavior of an animal in its natural habitat is considered natural observation.
Controlled Observation: It is the type of observation performed under supervision. The researcher controls the variables and effectively manages the environment in which the observation will be made. This method is widely used in laboratory research.
Participatory Observation: It is a type of observation in which the researcher actively participates and takes part in the group or community he is observing. In this way, the researcher can gain a deeper understanding by experiencing the phenomenon. For example, participant observation can be used to observe interactions within a social group.
Observation Log: It is a diary in which the data obtained during the observation are recorded. The observation log provides a detailed record of the event or phenomenon by the observer. This diary can be an important reference source for further analysis.
Scaling: While observing, it may be necessary to make some measurements and evaluations. This means expressing certain properties or variables on a numerical scale. For example, measuring the weight of an object in an experiment or evaluating a student’s behavior on a certain scale includes scaling techniques.
Covert Observation: In this type of observation, the observer observes himself without being noticed by the observed person or group. Thus, it is aimed to exhibit natural behaviors. Covert observation may be preferred in some cases to obtain more objective results.
Systematic Observation: It is a type of observation based on a certain plan or protocol. Researchers create a system to monitor certain variables and make certain measurements. In this way, the observation process becomes more structured and standardized.
Data Analysis: It is the stage of analyzing the data obtained from the observation process. This includes steps such as classifying data, identifying patterns, and drawing conclusions. Data analysis is important for the scientific evaluation of observation.
These basic concepts and techniques represent common approaches and methods used in making observations. Because observation is an important step in scientific research, using the right concepts and techniques is important to obtain reliable results.
Creating a Hypothesis
Hypothesizing is a method used in scientific research and is based on observation and thought processes. A hypothesis enables the researcher to ask a question about the phenomenon, make a hypothesis, and offer a testable explanation.
In the hypothesis-forming phase, the researcher makes a suggestion or prediction to understand or explain the phenomenon. This recommendation is an assumption derived from observation and available information. The hypothesis helps the researcher develop a theory or explanation for the phenomenon and provides a testable statement in future research and experiments.
Hypotheses are often expressed in formulations such as “if-then” or “X affects Y”. For example, statements such as “If plant A is given more sunlight, its growth rate will increase” or “High sound levels negatively affect people’s concentration” are examples of hypotheses.
Hypothesizing is a fundamental step in scientific research because it gives the researcher the opportunity to identify the questions to pose and the hypotheses to test. Hypotheses guide the researcher to organize experiments, collect data, and analyze results. Testability and verifiability are fundamental principles; that is, hypotheses must be testable by experiments or observations and their results verifiable.
By testing hypotheses, researchers learn more about the nature, relationships, or consequences of the phenomenon. Hypotheses may turn out to be true or produce contradictory results. In both cases, hypotheses contribute to the development of scientific knowledge and can trigger the process of further research and discovery.
Examples for a good hypothesis
“If the duration of sleep is reduced, individuals’ ability to concentrate decreases.”
This hypothesis tests the relationship between sleep duration and concentration. It is predicted that with the decrease of sleep time, the ability to concentrate will decrease. This hypothesis can be used to experimentally or observationally test the relationship between sleep duration and concentration.
“If plant A is given more fertilizer, the plant’s growth rate increases.”
This hypothesis tests the relationship between plant growth and fertilizer amount. It is predicted that the plant growth rate will increase with more fertilizer application. This hypothesis can be tested through plant experiments and provide data to determine the effect of fertilizer amount on plant growth.
“If an energy drink is consumed before exercise, sports performance improves.”
This hypothesis tests the relationship between energy drink consumption and sports performance. It is predicted that sports performance will increase by drinking energy drinks. This hypothesis aims to examine the effect of energy drink on performance by conducting experiments on athletes.
“If the air temperature rises, the ice is expected to melt faster.”
This hypothesis tests the relationship between air temperature and the rate at which ice melts. It is predicted that as the air temperature increases, the ice will melt faster. This hypothesis could investigate the effect of air temperature on the rate of ice melting by observation in the laboratory or natural environment.
These examples illustrate the characteristics of a good hypothesis. Each hypothesis provides a statement to test the effect of one particular variable on another and provides testability. A good hypothesis forms the basis of scientific research and contributes to the development of scientific knowledge by being supported by accurate tests.
Experiment Design and Implementation
Experiment design and execution refers to the planning and performance of a controlled experiment to test a hypothesis or study a phenomenon. An experiment consists of a series of steps designed and organized to confirm or disprove a hypothesis.
The experiment design and implementation process includes the following steps:
Research and Literature Review: Before starting the experiment, it is important to do research on the relevant topic and review the literature. This allows you to understand the results of similar previous experiments, relevant theories, and current knowledge.
Goal Setting: It is important to determine the purpose and objectives of the experiment. This allows you to clearly define what you want to test and what results you hope to achieve.
Identification of Variables: It is important to determine the variables in the experiment. The independent variable is the variable that the researcher controls and manipulates. The dependent variable is the effect observed on the results of the independent variable. Control variables are those that are kept constant to limit other effects and make the results of the experiment more reliable.
Experiment Design: It is important to plan how the experiment will be conducted. Experimental design ensures that the experiment is conducted in a controlled and reproducible manner. At this stage, experimental groups (experimental and control groups) and experimental conditions are determined.
Data Collection: Data collection methods are determined and applied during the experiment. Data can be collected through measurements, observations or questionnaires used in the experiment. In the data collection process, it is important to follow the experiment accurately and record the data.
Execution of the Experiment: In accordance with the experimental design, the test is carried out under the specified test conditions. During the experiment, it is ensured that the data is collected correctly and the experiment is checked when necessary.
Data Analysis and Results: The data collected after the experiment is analyzed. This enables the evaluation of data, conclusions, and confirmation or refutation of the hypothesis by statistical methods. Analysis results show the purpose of the experiment and whether it supports the hypothesis.
The process of designing and implementing experiments is an important step in scientific research used to test hypotheses, understand phenomena, and expand knowledge. An accurate experiment design and application is important to obtain reliable results and to contribute to scientific knowledge.
Experiment design steps and methods
Research and Question Formulation: Start by researching and examining available information on the relevant topic. In this process, identify the purpose of your research and formulate a question/hypothesis. Your question or hypothesis should define the focus of your experiment.
Determination of Independent and Dependent Variables: The independent variable is the variable that is controlled and manipulated in the experiment. The dependent variable is the effect observed on the results of the independent variable. In this step, determine which variable is independent and which variable is dependent.
Creating a Control Group and an Experimental Group: Determine the groups where you will perform your experiment. Usually, a control group and an experimental group are formed. The experimental group may be the group that is exposed to the influence of the independent variable, while the control group serves as the group kept under normal conditions, where you will not make any changes in the experiment.
Planning the Experiment Design: Experiment design is the stage of planning how the experiment will be carried out. Determine what steps the experiment will involve and in what order. Plan carefully so that the experiment is controlled, reproducible, and reliable.
Data Collection Methods: Determine how data will be collected during the experiment. This can be accomplished through measurements, observations, surveys or other data collection tools. The data collection process should be orderly and systematic to accurately track the experiment and record data.
Experiment Execution: Perform the experiment in accordance with the designed steps and conditions. Apply the independent variable to the control and experimental groups and observe how the dependent variable is affected.
Data Analysis and Inference: Analyze the data obtained as a result of the experiment. Evaluate your data and interpret the results using statistical methods. This indicates whether your hypothesis was confirmed or the results of your experiment.
Evaluation and Reporting of Results: Carefully evaluate the results of the experiment and draw your conclusions based on the results you have obtained. Discuss the significance of your experiment, the meaning of your results, and any limitations. Report your experiment and share your findings.
These steps represent an overall experiment design process. However, depending on the subject and purpose of the experiment, customized steps or additional details may be required. In addition, there may be differences in the process of designing experiments according to disciplines.
Data Collection and Analysis
Data collection and analysis refers to the processes involved in collecting, organizing, analyzing and interpreting data obtained as part of a research or experiment. This process enables the obtained data to be transformed into meaningful and specific results.
Data collecting:
Data Collection Plan: Before starting the data collection process, it is important to create the data collection plan. This plan specifies what data will be collected, how it will be collected, what tools will be used, and what processes it will include.
Data Collection Tools: Identify the tools you will use to collect data. These tools can be various methods such as questionnaires, scales, observation forms, interviews or measuring devices. When choosing tools, choose those that are appropriate for your data collection purpose, sampling methods, and ability to analyze your data.
Data Collection Process: During the data collection process, collect data using selected tools. In this process, make sure that the data is collected correctly in terms of consistency, accuracy and reliability. Throughout the data collection process, relevant protocols such as confidentiality, ethics and permissions must be followed.
Data analysis:
Data Editing and Coding: Organize and encode collected data. This allows data to be classified and organized in a meaningful way. Bring your data into an easily analyzable format and correct missing or incorrect data as needed.
Data Analysis Methods: Choose appropriate analysis methods to analyze your data. This may include different techniques such as statistical analysis, graphs, tables or content analysis. The methods chosen should be appropriate to the purpose of your research, the type of data, and your research questions.
Analysis and Interpretation of Data: Carefully review and interpret analysis results. This is essential for understanding statistical significance, patterns, relationships, and the meaning of findings. Evaluate the data against your research questions and hypotheses and make inferences based on the results.
Reporting Results: Report the analysis results in writing or orally. This allows you to present your findings in a clear and understandable way. Highlight key findings in the reporting process, along with graphs, tables, and statistical results that support your data.
Data collection and analysis is an important part of scientific research. This process enables the obtained data to gain meaning and to reach the results for the purpose of the research. Using the right data collection methods and choosing the appropriate techniques in the analysis process are critical to obtaining reliable results.
Evaluation and Interpretation of Results
Evaluating and interpreting the results is an important step in the data analysis process. At this stage, the results of the analysis are carefully examined in order to evaluate the obtained data in a meaningful way and to draw conclusions. Here are the steps for evaluating and interpreting the results:
Review of Analysis Results: First, carefully examine the analysis results. This includes findings from methods such as statistical analysis, graphs, tables or content analysis. Observe your data carefully and combine data and methods to understand analysis results.
Comparison with Research Questions and Hypotheses: Evaluate the analysis results against your research questions and hypotheses. This allows you to determine whether the results obtained support your questions and hypotheses. If the results support the predicted hypotheses or expected findings, you can interpret these results as confirmation. However, if the results are inconsistent or contradictory, it is important to delve deeper and explore possible explanations.
Evaluation of Statistical Significance: If statistical analyzes were performed, evaluate the statistical significance of the results. This can be done using p-values, confidence intervals, or statistical tests. Statistical significance helps determine whether the results are statistically significant based on your research questions and hypotheses.
Highlighting Key Findings: Identify and highlight key findings in analysis results. This allows you to identify the most important and meaningful results for the purpose of the research. Based on key findings, you can link to the literature in the research area and contribute to existing knowledge.
Description and Interpretation of Results: Explain and interpret results. Relate analysis results to the overall context of your research. This includes explaining the causes, mechanisms or relationships of the results and linking them to relevant theories. Interpret the data analysis results for the purpose of your research and your research questions.
Identifying Limitations and Future Research: Identify limitations and limitations of analysis results. This may include the scope of the research, methods of data collection or analysis. Also identify gaps or suggested areas for future research.
Evaluating and interpreting the results helps you understand the meaning and significance of the data obtained. These steps enable you to evaluate the analysis results in a systematic and logical way and to reach conclusions that fit the purpose of your research. Supporting and explaining your comments with evidence increases the proper understanding of the results and the impact of your research.
Scientific Communication
Scientific communication is the process of sharing and disseminating scientific knowledge. In this process, scientists communicate their research results and findings to other scientists, the academic community, or the general public. Scientific communication can be carried out through different communication channels such as scientific articles, conferences, seminars, poster presentations, books, magazines, popular science articles, websites and social media.
The main objectives of scientific communication are:
Information Sharing: Scientific communication enables the sharing of research results and scientific findings with other scientists and the academic community. This allows knowledge and discoveries to be evaluated and discussed by other researchers for innovation and progress.
Dissemination of New Information: Scientific communication enables new discoveries and scientific information to reach large masses. This enables the scientific community and the public to update existing knowledge and access innovations.
Communication and Cooperation: Scientific communication provides communication and cooperation between researchers. The sharing of research encourages the establishment of links between different disciplines and the development of joint projects.
Communication of Scientific Ethics and Standards: Scientific communication ensures the transfer of scientific ethics and research standards to other scientists and new generation researchers. This supports the healthy conduct of scientific research in terms of accuracy, reliability and compliance with ethical rules.
Scientific communication requires the accurate and understandable transfer of research results. For this, it is important that scientific writings are in a clear, consistent and logical structure, present data accurately, use appropriate references, and use scientific terminology effectively. At the same time, openness, transparency and reliability are important values in scientific communication.
Scientific communication enables researchers as a part of the scientific community to share their scientific findings and advance in the field of science. This process plays a critical role in the development of science and society’s access to scientific knowledge.
