Here is an illustration of an Action Hypothesis stated in different forms. Carefully observe the wordings, the format, relationship between the factors in each form of the hypothesis. Predictive form Declarative orDirectional Form QuestionForm Null Form If the III grade students receive a “drill work” in the chapter “Addition of whole numbers their progress will be better in Arithmetic. 1. Replace the word “Drill Work” as ‘Supervised study’ in all the forms. 2. Add after, addition of two digit (carrying) A “Drill work” program in the chapter addition of whole numbers for III grade students will cause/influence better progress in Arithmetic, Or Addition (whole number) drill work in and progress in Arithmetic are (positively) related to each other.OrThere is a (positive) relationship between ‘Drill work’ in Addition (whole Nos.) and progress in Arithmetic. To what extent a “Drill work” program in the chapter Addition (Whole numbers) for III grade students will improve their progress in Arithmetic.OrDoes a drill work program in ‘Addition (Whole Nos.) for III graders improve their progress in Arithmetic? If so, to what extent? A “Drill work” program in the chapter. ‘Addition for III grade students and their progress in Arithmetic are not related to each other.OrThere is no significant relationship between the ‘drill work’ program in the chapter addition and progress (whole No.) in Arithmetic among III grade students.
Eight students in class IV are not able to identify directions on a map. You have realized that inadequate exposure to map reading is the cause for the problem. Now write a hypothesis for finding a solution to this problem in all the four forms. Your course of remedial action should be reflected in the hypothesis.
|
Activity Sheet on Formulation of action hypothesis
| | | | | | | | | | | | |
| | | | | |
| | | | | |
| | | | | | | | | |
| | | | | | | | | | | | | | | |
Personal tools.
Thesis dialogue blueprint, writing wizard's template, research proposal compass.
This comprehensive guide explores the intricacies of formulating research questions and hypotheses across various academic disciplines. By delving into examples and methodological approaches, the article aims to provide scholars and researchers with the tools necessary to develop robust and effective research frameworks. Understanding and crafting well-formed research questions and hypotheses are pivotal in conducting meaningful research that can significantly contribute to knowledge within a field.
Defining research questions.
Research questions are the backbone of any scholarly inquiry, guiding you through the exploration of your chosen topic. They help you focus your study and determine the direction of your research. A well-crafted research question should be clear, focused, and answerable within the constraints of your study.
A strong hypothesis provides a specific, testable prediction about the expected outcomes of your research. It is not merely a guess but is grounded in existing literature and theory. To develop a robust hypothesis, consider the variables involved and ensure that it is feasible to test them within your study's design.
Understanding the interrelation between research questions and hypotheses is crucial for structuring your research effectively. Your hypothesis should directly address the gap in the literature highlighted by your research question, providing a clear pathway for investigation. This alignment ensures that your study can contribute valuable insights to your field.
Identifying the purpose.
To craft an effective research question , you must first identify the purpose of your study. This involves understanding what you aim to discover or elucidate through your research. Ask yourself what the core of your inquiry is and what outcomes you hope to achieve. This clarity will guide your entire research process, ensuring that your question is not only relevant but also deeply rooted in your specific academic or practical goals.
It's crucial to define the scope and limitations of your research early on. This helps in setting realistic boundaries and expectations for your study. Consider factors such as time, resources, and the breadth of the subject area. Narrowing down your focus to a manageable scope can prevent the common pitfall of an overly broad or vague question, which can dilute the impact of your findings.
The final step in crafting your research question is formulating it in a way that drives inquiry. This means your question should be clear, concise, and structured to prompt detailed investigation and critical analysis. It should challenge existing knowledge and push the boundaries of what is already known. Utilizing strategies like the Thesis Dialogue Blueprint or the Research Proposal Compass can be instrumental in refining your question to ensure it is both innovative and feasible.
From research questions to hypotheses.
When you transition from research questions to hypotheses, you are essentially moving from what you want to know to what you predict will happen. This shift involves formulating a specific, testable prediction that directly stems from your initial question. Ensure your hypothesis is directly linked to and derived from your research question to maintain a coherent research strategy.
There are several types of hypotheses you might encounter, including simple, complex, directional, nondirectional, associative, causal, null, and alternative. Each type serves a different purpose and is chosen based on the specifics of the research question and the nature of the study. For instance, a null hypothesis might be used to test the effectiveness of a new teaching method compared to the standard.
Testing your hypothesis is a critical step in the research process. This phase involves collecting data, conducting experiments, or utilizing other research methods to determine the validity of your hypothesis. After testing, you may find that your hypothesis needs refining or even reformation based on the outcomes. This iterative process is essential for narrowing down the most accurate explanation or prediction for your research question.
Humanities and social sciences.
In the realm of Humanities and Social Sciences, research questions often explore cultural, social, historical, or philosophical aspects. How does gender representation in 20th-century American literature reflect broader social changes? This question not only seeks to uncover specific literary trends but also ties them to societal shifts, offering a rich field for analysis.
Research questions in the Natural Sciences are typically aimed at understanding natural phenomena or solving specific scientific problems. A common question might be, What are the effects of plastic pollutants on marine biodiversity? This inquiry highlights the environmental concerns and seeks empirical data to understand the impact.
In Applied Sciences, the focus is often on improving technology or engineering solutions. A pertinent question could be, How can renewable energy sources be integrated into existing power grids? This question addresses the practical challenges and potential innovations in energy systems, crucial for advancing sustainable technologies.
Case studies in psychology.
In psychology, hypotheses often explore the causal relationships between cognitive functions and behaviors. Consider how a hypothesis might predict the impact of stress on memory recall . By examining various case studies, you can see how hypotheses are specifically tailored to address intricate psychological phenomena.
Biology experiments frequently test hypotheses about physiological processes or genetic information. For instance, a hypothesis might propose that a specific gene influences plant growth rates. Through rigorous testing, these hypotheses contribute significantly to our understanding of biological systems.
Field studies in environmental science provide a rich ground for testing hypotheses related to ecosystem dynamics and conservation strategies. A common hypothesis might explore the effects of human activity on biodiversity. These studies often involve complex data collection and analysis, highlighting the interrelation between empirical evidence and theoretical predictions.
Quantitative vs. qualitative research.
When you embark on hypothesis formulation, understanding the distinction between quantitative and qualitative research methodologies is crucial. Quantitative research focuses on numerical data and statistical analysis, ideal for hypotheses that require measurable evidence. In contrast, qualitative research delves into thematic and descriptive data, providing depth and context to hypotheses that explore behaviors, perceptions, and experiences.
Theoretical frameworks serve as the backbone for developing robust hypotheses. They provide a structured way to align your hypothesis with existing knowledge. By integrating theories and models relevant to your study, you ensure that your hypothesis has a solid foundation and aligns with established academic thought.
A thorough review of existing literature is indispensable for crafting a well-informed hypothesis. This process not only highlights gaps in current research but also allows you to build on the work of others. By synthesizing findings from previous studies, you can formulate hypotheses that are both innovative and grounded in academic precedent.
On research outcomes.
Understanding the impact of well-formed research questions and hypotheses on research outcomes is crucial. Well-crafted questions and hypotheses serve as a framework that guides the entire research process , ensuring that the study remains focused and relevant. They help in defining the scope of the study and in identifying the variables that need to be measured, thus directly influencing the validity and reliability of the research findings.
The role of well-defined research questions and hypotheses extends beyond the research process into the realm of academic publishing. A clear hypothesis provides a strong foundation for the research paper, enhancing its chances of acceptance in prestigious journals. The clarity and direction afforded by a solid hypothesis make the research more appealing to a scholarly audience, potentially increasing citation rates and academic recognition.
When applying for research grants, the clarity of your research questions and hypotheses can significantly impact the decision-making process of funding bodies. A well-articulated hypothesis demonstrates a clear vision and a structured approach to addressing a specific issue, which can be crucial in securing funding. Grant reviewers often look for proposals that promise substantial contributions to the field, and a strong hypothesis can be a key factor in showcasing the potential impact of your research.
In our latest article, 'Evaluating the Impact of Well-Formed Research Questions and Hypotheses,' we delve into the crucial role that precise questions and hypotheses play in academic research. Understanding this can significantly enhance your thesis writing process. For a deeper exploration and practical tools to apply these concepts, visit our website and discover how our Thesis Action Plan can transform your academic journey. Don't miss out on our special offers tailored just for you!
In this comprehensive guide, we have explored various examples of research questions and hypotheses, shedding light on their significance and application in academic research. Understanding the distinction between a research question and a hypothesis, as well as knowing how to effectively formulate them, is crucial for conducting methodical and impactful studies. By examining different scenarios and examples, this guide aims to equip researchers with the knowledge to craft well-defined research questions and hypotheses that can drive meaningful investigations and contribute to the broader field of knowledge. As we continue to delve into the intricacies of research design, it is our hope that this guide serves as a valuable resource for both novice and experienced researchers in their scholarly endeavors.
What is a research question.
A research question is a clearly defined query that guides a scientific or academic study. It sets the scope and focus of the research by asking about a specific phenomenon or issue.
A hypothesis is a specific, testable prediction about what will happen in a study based on prior knowledge or theory, while a research question is an open query that guides the direction of the investigation.
A strong hypothesis is clear, testable, based on existing knowledge, and it states an expected relationship between variables.
Research questions define the scope of inquiry, while hypotheses provide a specific prediction about the expected outcomes that can be tested through research methods.
When formulating a research question, consider clarity, focus, relevance, and the feasibility of answering the question through available research methods.
A well-formed hypothesis directs the research process, allows for clear testing of assumptions, and helps in drawing meaningful conclusions that can contribute to the body of knowledge.
How to structure your master thesis for success, master thesis help: resources and advice for success, how to plan a dissertation: a step-by-step guide, how to write the best dissertation: tips and strategies.
© 2024 Research Rebels, All rights reserved.
Your cart is currently empty.
Run a free plagiarism check in 10 minutes, generate accurate citations for free.
Published on November 8, 2019 by Rebecca Bevans . Revised on June 22, 2023.
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics . It is most often used by scientists to test specific predictions, called hypotheses, that arise from theories.
There are 5 main steps in hypothesis testing:
Though the specific details might vary, the procedure you will use when testing a hypothesis will always follow some version of these steps.
Step 1: state your null and alternate hypothesis, step 2: collect data, step 3: perform a statistical test, step 4: decide whether to reject or fail to reject your null hypothesis, step 5: present your findings, other interesting articles, frequently asked questions about hypothesis testing.
After developing your initial research hypothesis (the prediction that you want to investigate), it is important to restate it as a null (H o ) and alternate (H a ) hypothesis so that you can test it mathematically.
The alternate hypothesis is usually your initial hypothesis that predicts a relationship between variables. The null hypothesis is a prediction of no relationship between the variables you are interested in.
Professional editors proofread and edit your paper by focusing on:
See an example
For a statistical test to be valid , it is important to perform sampling and collect data in a way that is designed to test your hypothesis. If your data are not representative, then you cannot make statistical inferences about the population you are interested in.
There are a variety of statistical tests available, but they are all based on the comparison of within-group variance (how spread out the data is within a category) versus between-group variance (how different the categories are from one another).
If the between-group variance is large enough that there is little or no overlap between groups, then your statistical test will reflect that by showing a low p -value . This means it is unlikely that the differences between these groups came about by chance.
Alternatively, if there is high within-group variance and low between-group variance, then your statistical test will reflect that with a high p -value. This means it is likely that any difference you measure between groups is due to chance.
Your choice of statistical test will be based on the type of variables and the level of measurement of your collected data .
Based on the outcome of your statistical test, you will have to decide whether to reject or fail to reject your null hypothesis.
In most cases you will use the p -value generated by your statistical test to guide your decision. And in most cases, your predetermined level of significance for rejecting the null hypothesis will be 0.05 – that is, when there is a less than 5% chance that you would see these results if the null hypothesis were true.
In some cases, researchers choose a more conservative level of significance, such as 0.01 (1%). This minimizes the risk of incorrectly rejecting the null hypothesis ( Type I error ).
The results of hypothesis testing will be presented in the results and discussion sections of your research paper , dissertation or thesis .
In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p -value). In the discussion , you can discuss whether your initial hypothesis was supported by your results or not.
In the formal language of hypothesis testing, we talk about rejecting or failing to reject the null hypothesis. You will probably be asked to do this in your statistics assignments.
However, when presenting research results in academic papers we rarely talk this way. Instead, we go back to our alternate hypothesis (in this case, the hypothesis that men are on average taller than women) and state whether the result of our test did or did not support the alternate hypothesis.
If your null hypothesis was rejected, this result is interpreted as “supported the alternate hypothesis.”
These are superficial differences; you can see that they mean the same thing.
You might notice that we don’t say that we reject or fail to reject the alternate hypothesis . This is because hypothesis testing is not designed to prove or disprove anything. It is only designed to test whether a pattern we measure could have arisen spuriously, or by chance.
If we reject the null hypothesis based on our research (i.e., we find that it is unlikely that the pattern arose by chance), then we can say our test lends support to our hypothesis . But if the pattern does not pass our decision rule, meaning that it could have arisen by chance, then we say the test is inconsistent with our hypothesis .
If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.
Methodology
Research bias
Hypothesis testing is a formal procedure for investigating our ideas about the world using statistics. It is used by scientists to test specific predictions, called hypotheses , by calculating how likely it is that a pattern or relationship between variables could have arisen by chance.
A hypothesis states your predictions about what your research will find. It is a tentative answer to your research question that has not yet been tested. For some research projects, you might have to write several hypotheses that address different aspects of your research question.
A hypothesis is not just a guess — it should be based on existing theories and knowledge. It also has to be testable, which means you can support or refute it through scientific research methods (such as experiments, observations and statistical analysis of data).
Null and alternative hypotheses are used in statistical hypothesis testing . The null hypothesis of a test always predicts no effect or no relationship between variables, while the alternative hypothesis states your research prediction of an effect or relationship.
If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.
Bevans, R. (2023, June 22). Hypothesis Testing | A Step-by-Step Guide with Easy Examples. Scribbr. Retrieved July 1, 2024, from https://www.scribbr.com/statistics/hypothesis-testing/
Other students also liked, choosing the right statistical test | types & examples, understanding p values | definition and examples, what is your plagiarism score.
Emergent Learning
What are action hypotheses.
Action Hypotheses are a form of “if/then” hypothesis that we use in Emergent Learning. While scientific hypotheses propose an explanation of phenomena based on evidence from the past, action hypotheses look ahead to explain what we expect to happen as a result of future action. In both cases, the goal is to articulate something that is testable.
Action Hypotheses explore the thinking about a result we hope to accomplish (the “then”) and what we think it will take to get there (the “if”). This kind of hypothesis is so fundamental to our thinking every day that we couldn’t take action without it. It’s the smallest kernel of how we think. For example:
IF… | THEN… |
---|---|
I step outside and take a short walk, | I will be able to pay better attention in the second part of this meeting. |
We make general operating support grants, | Our grantees will have the flexibility to implement solutions that are most important in their contexts. |
We invest in research and programming that reveal the increased disparities caused through public and private insurance payments, | Policymakers will adopt payment changes that advance equity. |
When people in an Emergent Learning culture say, “my hypothesis is…”, it is not to demonstrate authority by citing scientific language. Rather, they are communicating in shorthand that “I am making my thinking transparent to you and inviting you to inquire about it or offer an alternative.” It is intended to grow our collective thinking and agency.
EL practitioners learn to listen for hypotheses in conversations and ask questions to make sure we understand the thinking of our colleagues in a team or initiative. We use them to tease out the thinking implicit in Theories of Change. We read for them in narratives and use them to write stronger narratives. We use them to create more powerful connections between learning and evaluation by expressing hypotheses and planning how to test them out in our work over time, so that we track not just results but learn about and refine the thinking that got us there.
Hypotheses also play a starring role in Emergent Learning Tables. The whole aim is to collect all of our experience and data and make meaning of it in order to express our best possible thinking about what it will take to succeed going forward — expressed in the form of one or more Action Hypotheses.
Thinking of strategies or actions as “hypotheses” reminds us that what we thought was “true” or “right” might not be. Using this language invites us to remember that there is usually more than one path to consider and that maybe it’s OK to hold and test more than one hypothesis at a time — as long as we actually circle back and test them!
In order to continue enjoying our site, we ask that you confirm your identity as a human. Thank you very much for your cooperation.
Cite this chapter.
168 Accesses
The term hypothesis has been mentioned several times in the preceding chapters. The definition that will be used here is that a hypothesis is a proposition set forth as explanation for the occurrence of a specified phenomenon. The basis of scientific investigation is the collection of information that is used either to formulate or to test hypotheses. One assesses the important variables and tries to build a model or hypothesis that explains the observed phenomenon. In general, a hypothesis is formulated by rephrasing the objective of a study as a statement, e.g., if the objective of an investigation is to determine if a pesticide is safe, the resulting hypothesis might be “ the pesticide is not safe ”, or alternatively that “ the pesticide is safe ”. A hypothesis is a statistical hypothesis only if it is stated in terms related to the distribution of populations. The general hypothesis above might be refined to: “ this pesticide, when used as directed, has no effect on the average number of robins in an area ”, which is a testable hypothesis. The hypothesis to be tested is called the null hypothesis (H 0 ). The alternative hypothesis (H 1 ) for the above example would be “ this pesticide, when used as directed, has an effect on the average number of robins in an area”. In testing a hypothesis, H 0 is considered to be true, unless the sample data indicate otherwise, (i.e., that the pesticide is innocent, unless proven guilty). Testing cannot prove H 0 to be true but the results can cause it to be rejected. In accepting or rejecting H 0 , two types of error may be made. If H 0 is rejected when, in fact, it is true a type 1 error has been committed. If Ho is not true and the test fails to reject it, a type 2 error has been made.
“ Research in the field, through study of disease as it manifests itself in nature, is an important and independent approach to solution of medical problems. Modern medical progress has been so thoroughly associated with research in the biological laboratory, and it has been so largely a development of the experimental method, that this other and older method has come in recent years to be overshadowed ” (Gordon, 1950)
This is a preview of subscription content, log in via an institution to check access.
Subscribe and save.
Tax calculation will be finalised at checkout
Purchases are for personal use only
Institutional subscriptions
Unable to display preview. Download preview PDF.
Authors and affiliations.
Western College of Veterinary Medicine, University of Saskatchewan, Saskatoon, Canada
Gary A. Wobeser
You can also search for this author in PubMed Google Scholar
Reprints and permissions
© 1994 Springer Science+Business Media New York
Wobeser, G.A. (1994). Formulating and Testing Hypotheses. In: Investigation and Management of Disease in Wild Animals. Springer, Boston, MA. https://doi.org/10.1007/978-1-4757-5609-8_6
DOI : https://doi.org/10.1007/978-1-4757-5609-8_6
Publisher Name : Springer, Boston, MA
Print ISBN : 978-1-4757-5611-1
Online ISBN : 978-1-4757-5609-8
eBook Packages : Springer Book Archive
Anyone you share the following link with will be able to read this content:
Sorry, a shareable link is not currently available for this article.
Provided by the Springer Nature SharedIt content-sharing initiative
Policies and ethics
You are accessing a machine-readable page. In order to be human-readable, please install an RSS reader.
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess .
Feature papers represent the most advanced research with significant potential for high impact in the field. A Feature Paper should be a substantial original Article that involves several techniques or approaches, provides an outlook for future research directions and describes possible research applications.
Feature papers are submitted upon individual invitation or recommendation by the scientific editors and must receive positive feedback from the reviewers.
Editor’s Choice articles are based on recommendations by the scientific editors of MDPI journals from around the world. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal.
Original Submission Date Received: .
Find support for a specific problem in the support section of our website.
Please let us know what you think of our products and services.
Visit our dedicated information section to learn more about MDPI.
Formulation and stability assessment of bakery snacks enriched with encapsulated phenolic compounds from lemnian tomatoes and pumpkin ( cucurbita moschata ).
2. materials and methods, 2.1. chemicals and reagents, 2.2. sample preparation, 2.3. extraction of samples assisted by ultrasounds, 2.4. evaluation of antioxidant activity in extracted samples, 2.5. determination of total phenolics, 2.6. statistical analysis, 2.7. the design of experimentation, 2.8. verification of the statistical model, 2.9. encapsulation of the optimized extracts, encapsulation yield and efficiency, 2.10. preparation of the enriched bakery product, 2.11. stability evaluation, 3.1. model fitting, 3.2. extraction optimization and model validation, 3.3. determination of antioxidant capacity and total phenolics, 3.4. encapsulation efficiency and yield evaluation, 3.5. stability evaluation for the extracts, 3.6. enriched products evaluation, 4. discussion, 5. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
Click here to enlarge figure
Factor Levels and Range | ||||
---|---|---|---|---|
Factors | Codes | −1 | 0 | 1 |
Time (min) | X | 20 | 40 | 60 |
Ethanol (%, v/v) | X | 30 | 50 | 70 |
Ingredients | Quantity in Grams |
---|---|
Bread flour | 112.0 |
Sugar | 2.00 |
Olive oil | 5.40 |
Fast action yeast | 0.60 |
Sea salt | 1.70 |
Water | 50.0 |
Ingredients | Quantity in Grams |
---|---|
Oat flakes | 82.0 |
Sugar | 0.50 |
Olive oil | 10.8 |
Fast action yeast | 0.60 |
Sea salt | 1.70 |
Water | 36.6 |
Run | Independent Factors | Response TPC (mg/g) | ||
---|---|---|---|---|
X Ethanol (% v/v) | X Time (min) | Tomato Extract | Pumpkin Extract | |
1 | 50 (0) | 40 (0) | 9.91 | 2.79 |
2 | 30 (−1) | 20 (−1) | 3.12 | 3.98 |
3 | 50 (0) | 20 (−1) | 5.56 | 2.45 |
4 | 50 (0) | 40 (0) | 8.91 | 3.15 |
5 | 30 (−1) | 40 (0) | 4.21 | 4.35 |
6 | 50 (0) | 40 (0) | 9.25 | 3.04 |
7 | 70 (+1) | 20 (−1) | 3.24 | 0.98 |
8 | 70 (+1) | 40 (0) | 5.12 | 2.69 |
9 | 50 (0) | 60 (+1) | 6.76 | 1.68 |
10 | 70 (+1) | 60 (+1) | 6.25 | 1.89 |
11 | 50 (0) | 40 (0) | 8.56 | 2.56 |
12 | 30 (−1) | 60 (+1) | 2.95 | 2.55 |
13 | 50 (0) | 40 (0) | 8.94 | 2.88 |
Tomato | Pumpkin | ||||
---|---|---|---|---|---|
Source | DF | F-Value | p-Value | F-Value | p-Value |
Model | 5 | 92.71 | 0.000 | 32.61 | 0.000 |
Linear | 2 | 151.38 | 0.000 | 45.62 | 0.000 |
Ethanol | 1 | 251.73 | 0.000 | 34.14 | 0.001 |
time | 1 | 25.61 | 0.001 | 67.90 | 0.000 |
Square | 2 | 185.50 | 0.000 | 24.44 | 0.001 |
Ethanol × Ethanol | 1 | 187.21 | 0.000 | 10.78 | 0.013 |
time × time | 1 | 53.62 | 0.000 | 48.41 | 0.000 |
2-Way Interaction | 1 | 30.98 | 0.001 | 24.56 | 0.002 |
Ethanol × time | 1 | 30.98 | 0.001 | 24.56 | 0.002 |
Error | 7 | ||||
Lack-of-Fit | 3 | 5.48 | 0.067 | 1.16 | 0.429 |
Pure Error | 4 | ||||
Total | 12 |
TPC | Predictive Equations | R | R Adjusted | |
---|---|---|---|---|
Tomato | −Y = −1.3419 + 0.02943 ethanol + 0.00969 time − 0.000317 ethanol × ethanol − 0.000170 time × time + 0.000107 ethanol × time | (4) | 98.51% | 97.45% |
Pumpkin | Y = 7.47 − 0.2195 ethanol + 0.1138 time + 0.001166 ethanol × ethanol − 0.002471 time × time + 0.001463 ethanol × time | (5) | 95.88% | 92.94% |
Independent Factor TPC (mg/g DM) | Predicted Values | Experimental Values | Desirability |
---|---|---|---|
Tomato | 9.46 | 9.47 ± 0.08 | 0.9300 |
Pumpkin | 4.44 | 4.52 ± 0.05 | 1.0000 |
Parameters | Tomato Optimized Extract | Pumpkin Optimized Extract |
---|---|---|
DPPH (μmol TE/g) | 7.65 ± 0.08 | 5.78± 0.05 |
ABTS (μmol TE/g) | 9.27 ± 0.02 | 3.95 ± 0.04 |
FRAP (μmol TE/g) | 5.25 ± 0.09 | 2.99 ± 0.03 |
CUPRAC (μmol TE/g) | 2.3 ± 0.04 | 1.25 ± 0.05 |
TPC (mg GAE/g) | 9.47 ± 0.08 | 4.52 ± 0.05 |
Core–Coating Ratio (w/w) | Tomato Encapsulated Extract % Yield of Total Phenolics |
---|---|
1:10 | 72.5 ± 1.0 |
1:15 | 63.5 ± 0.3 |
1:20 | 62.2 ± 0.4 |
1:25 | 67.9 ± 0.7 |
1:30 | 89.2 ± 0.7 |
Days | Extract Stability Testing at 25 °C (%Content)/±SD | Stability Testing at 65 °C (%Content)/±SD | ||
---|---|---|---|---|
Crude | Encapsulated | Crude | Encapsulated | |
0 | 100.0 ± 0.4 | 100.0 ± 1.1 | 100.0 ± 0.4 | 100.0 ± 1.1 |
3 | 90.2 ± 1.1 | 97.1 ± 0.5 | 77.9 ± 0.5 | 98.1 ± 1.2 |
6 | 77.2 ± 1.0 | 96.2 ± 0.4 | 65.9 ± 0.9 | 92.8 ± 1.5 |
9 | 59.2 ± 0.7 | 93.1 ± 1.2 | 52.8 ± 0.8 | 89.1 ± 1.5 |
12 | 49.2 ± 0.9 | 89.8 ± 0.8 | 39.9 ± 1.1 | 77.4 ± 0.5 |
Days | Extract Stability Testing at 25 °C (%Content)/±SD | Stability Testing at 65 °C (%Content)/±SD | ||
---|---|---|---|---|
Crude | Encapsulated | Crude | Encapsulated | |
0 | 100.0 ± 0.8 | 100.0 ± 0.9 | 100.0 ± 0.8 | 100.0 ± 0.9 |
3 | 82.2 ± 0.7 | 99.1 ± 1.2 | 79.5 ± 1.1 | 95.4 ± 0.4 |
6 | 78.7 ± 0.6 | 92.3 ± 1.7 | 61.1 ± 1.7 | 87.8 ± 1.5 |
9 | 61.1 ± 0.9 | 88.4 ± 1.1 | 45.2 ± 1.3 | 81.7 ± 1.0 |
12 | 49.4 ± 0.8 | 79.9 ± 0.8 | 38.1 ± 0.5 | 71.8 ± 1.4 |
Test | Tomato Cereal Bars | Pumpkin Biscuits | ||||
---|---|---|---|---|---|---|
Enriched | Control | %Increase | Enriched | Control | %Increase | |
Total phenolic content | 1.51 ± 0.03 | 1.20 ± 0.01 | 25.8 | 1.72 ± 0.02 | 1.67 ± 0.03 | 3.0 |
DPPH | 3.67 ± 0.04 | 2.65 ± 0.03 | 38.5 | 3.37 ± 0.02 | 2.28 ± 0.04 | 47.8 |
ABTS | 11.63 ± 0.04 | 9.68 ± 0.02 | 20.2 | 9.55 ± 0.05 | 8.70 ± 0.03 | 9.7 |
FRAP | 17.59 ± 0.05 | 13.92 ± 0.06 | 26.4 | 9.81 ± 0.06 | 7.38 ± 0.04 | 32.9 |
CUPRAC | 19.35 ± 0.04 | 13.79 ± 0.02 | 40.3 | 11.75 ± 0.02 | 11.32 ± 0.03 | 3.8 |
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
Michalaki, A.; Karantonis, H.C. Formulation and Stability Assessment of Bakery Snacks Enriched with Encapsulated Phenolic Compounds from Lemnian Tomatoes and Pumpkin ( Cucurbita moschata ). Appl. Sci. 2024 , 14 , 5724. https://doi.org/10.3390/app14135724
Michalaki A, Karantonis HC. Formulation and Stability Assessment of Bakery Snacks Enriched with Encapsulated Phenolic Compounds from Lemnian Tomatoes and Pumpkin ( Cucurbita moschata ). Applied Sciences . 2024; 14(13):5724. https://doi.org/10.3390/app14135724
Michalaki, Afroditi, and Haralabos C. Karantonis. 2024. "Formulation and Stability Assessment of Bakery Snacks Enriched with Encapsulated Phenolic Compounds from Lemnian Tomatoes and Pumpkin ( Cucurbita moschata )" Applied Sciences 14, no. 13: 5724. https://doi.org/10.3390/app14135724
Article access statistics, further information, mdpi initiatives, follow mdpi.
Subscribe to receive issue release notifications and newsletters from MDPI journals
COMMENTS
3. Simple hypothesis. A simple hypothesis is a statement made to reflect the relation between exactly two variables. One independent and one dependent. Consider the example, "Smoking is a prominent cause of lung cancer." The dependent variable, lung cancer, is dependent on the independent variable, smoking. 4.
Action research is a research method that aims to simultaneously investigate and solve an issue. In other words, as its name suggests, action research conducts research and takes action at the same time. It was first coined as a term in 1944 by MIT professor Kurt Lewin.A highly interactive method, action research is often used in the social ...
6. Write a null hypothesis. If your research involves statistical hypothesis testing, you will also have to write a null hypothesis. The null hypothesis is the default position that there is no association between the variables. The null hypothesis is written as H 0, while the alternative hypothesis is H 1 or H a.
Formulating Hypotheses for Different Study Designs. Generating a testable working hypothesis is the first step towards conducting original research. Such research may prove or disprove the proposed hypothesis. Case reports, case series, online surveys and other observational studies, clinical trials, and narrative reviews help to generate ...
It seeks to explore and understand a particular aspect of the research subject. In contrast, a research hypothesis is a specific statement or prediction that suggests an expected relationship between variables. It is formulated based on existing knowledge or theories and guides the research design and data analysis. 7.
Your hypothesis is what you propose to "prove" by your research. As a result of your research, you will arrive at a conclusion, a theory, or understanding that will be useful or applicable beyond the research itself. 3. Avoid judgmental words in your hypothesis. Value judgments are subjective and are not appropriate for a hypothesis.
INTRODUCTION. Scientific research is usually initiated by posing evidenced-based research questions which are then explicitly restated as hypotheses.1,2 The hypotheses provide directions to guide the study, solutions, explanations, and expected results.3,4 Both research questions and hypotheses are essentially formulated based on conventional theories and real-world processes, which allow the ...
Building on the ideas in Chap. 1, we describe formulating, testing, and revising hypotheses as a continuing cycle of clarifying what you want to study, making predictions about what you might find together with developing your reasons for these predictions, imagining tests of these predictions, revising your predictions and rationales, and so ...
A hypothesis (from the Greek, foundation) is a logical construct, interposed between a problem and its solution, which represents a proposed answer to a research question. It gives direction to the investigator's thinking about the problem and, therefore, facilitates a solution. Unlike facts and assumptions (presumed true and, therefore, not ...
ACTION RESEARCH Session 2.3 Formulating and Evaluating Action Research Questions and Hypotheses 9 Research Hypothesis Research hypothesis is the tentative answer to the research question. It is the hypothesis of interest in the study, the statement that the Action Researcher wants to support. An
The steps to write a research hypothesis are: 1. Stating the problem: Ensure that the hypothesis defines the research problem. 2. Writing a hypothesis as an 'if-then' statement: Include the action and the expected outcome of your study by following a 'if-then' structure. 3.
A hypothesis is a tentative statement about the relationship between two or more variables. It is a specific, testable prediction about what you expect to happen in a study. It is a preliminary answer to your question that helps guide the research process. Consider a study designed to examine the relationship between sleep deprivation and test ...
These two criteria are translated into various activities of researchers through the research process. Unit 3 and Unit 4 intend to describe the research process in detail. Formulation of research problem, the first step in the research process, is considered as the most important phase of a research project. This step starts with the selection ...
A Hypothesis is a hunch or a shrewd guess or a tentative solution or an inference or sub-position to be tested by empirical evidences. Once the investigator diagnoses the causes of the pinpointed/specific problems, he/she starts thinking about what concrete action, if taken, would bring about the desired change/solution.
Testing your hypothesis is a critical step in the research process. This phase involves collecting data, conducting experiments, or utilizing other research methods to determine the validity of your hypothesis. After testing, you may find that your hypothesis needs refining or even reformation based on the outcomes.
Steps of Action Research. • A er lis ng the causes, a teacher nally se les. down with one of the causes as the potent one. and selects th at par cular cause to formulate. the hypothesis. IV ...
identify and determine the problem to study. Identifying a research problem is important. because, as the issue or concern in a particular setting that motivates and guides the need. Parlindungan ...
Step 5: Present your findings. The results of hypothesis testing will be presented in the results and discussion sections of your research paper, dissertation or thesis.. In the results section you should give a brief summary of the data and a summary of the results of your statistical test (for example, the estimated difference between group means and associated p-value).
Action Hypotheses are a form of "if/then" hypothesis that we use in Emergent Learning. While scientific hypotheses propose an explanation of phenomena based on evidence from the past, action hypotheses look ahead to explain what we expect to happen as a result of future action. In both cases, the goal is to articulate something that is ...
To formulate a hypothesis, a researcher must consider the requirements of a strong hypothesis: Make a prediction based on previous observations or research. Define objective independent and ...
The researcher states a hypothesis to be tested, formulates an analysis plan, analyzes sample data. according to the plan, and accepts or rejects the null hypothesis, based on r esults of the ...
A hypothesis is a statistical hypothesis only if it is stated in terms related to the distribution of populations. The general hypothesis above might be refined to: " this pesticide, when used as directed, has no effect on the average number of robins in an area ", which is a testable hypothesis. The hypothesis to be tested is called the ...
Research Hypothesis. A research hypothesis is a statement that predicts a specific relationship between two or more variables. It is typically used in experimental research to test cause-and-effect relationships. For example, if you were conducting a study on the effects of sleep deprivation on memory, your research hypothesis might be ...
In recent years, the health-promoting properties of plant-derived compounds have garnered increasing scientific interest. Notably, tomatoes and pumpkins (Cucurbita moschata), renowned for their abundant phytochemicals and associated biological activities, have become focal points of research. This study investigated the extraction of phenolic compounds from tomatoes and pumpkins cultivated on ...