We have heard of many hypotheses which have led to great inventions in science. Assumptions that are made on the basis of some evidence are known as hypotheses. In this article, let us learn in detail about the hypothesis and the type of hypothesis with examples.
A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables.
Following are the characteristics of the hypothesis:
Following are the sources of hypothesis:
There are six forms of hypothesis and they are:
It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.
It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.
It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.
It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.
It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “H O ”.
Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.
Following are the examples of hypotheses based on their types:
Following are the functions performed by the hypothesis:
Researchers use hypotheses to put down their thoughts directing how the experiment would take place. Following are the steps that are involved in the scientific method:
What is hypothesis.
A hypothesis is an assumption made based on some evidence.
What are the types of hypothesis.
Types of hypothesis are:
Define complex hypothesis..
A complex hypothesis shows the relationship between two or more dependent variables and two or more independent variables.
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Published on June 9, 2024 by Julia Merkus, MA .
An experimental design is a systematic plan for conducting an experiment that aims to test a hypothesis or answer a research question.
It involves manipulating one or more independent variables (IVs) and measuring their effect on one or more dependent variables (DVs) while controlling for other variables that could influence the outcome.
The goal of an experimental design is to isolate the effect of the independent variable on the dependent variable while controlling for other variables that could influence the outcome. By doing so, researchers can:
Your sample needs to be representative to draw valid conclusions from your data. If it’s unethical, hard, or even impossible to randomly assign participants to a control or treatment group, it’s best to use an observational design instead.
What is an experimental design, step 1: define your research question and variables, step 2: formulate a specific, testable hypothesis, step 3: develop experimental treatments, step 4: divide subjects between treatment and control groups, step 5: decide how to measure your dependent variable, types of experimental design, frequently asked questions about experimental design.
Experimental designs are used to investigate causal relationships by manipulating one or more independent variables and observing their impact on one or more dependent variables.
An experimental design involves a structured approach to testing a hypothesis. A thorough understanding of the study subject is essential for a well-designed experiment.
There are five crucial steps in designing an experiment:
The first step is to formulate the research question for your experimental design.
Experimental design example: Research question
You’re interested in how studying influences test scores. Specifically, you want to know how the number of hours a person studies before a test affects their test score.
In order to formulate a hypothesis, you first need to understand what the independent and dependent variables of your study are.
To create your experimental design, you also need to take into account extraneous variables, such as confounding variables, because you need to control for these in your design.
To visualize your design, you can combine the variables into an experimental design diagram . You use solid and dotted line arrows to indicate possible relationships, as well as plus and minus signs to show the predicted effect.
Your conceptual understanding of the phenomenon you’re studying allows you to formulate a testable hypothesis for your research question.
In most cases, you write a null hypothesis (H0) that predicts no relationship between the independent and dependent variables and an alternate hypothesis (H1 or Ha) that predicts a relationship between the variables.
The number of hours someone studies before a test does not correlate with their test score.
Alternate hypothesis
The next steps describe how to design a controlled experiment, where you must be able to measure the dependent variable(s) precisely, control for any extraneous variables, and systematically and accurately manipulate the independent variable(s).
If your topic or set-up doesn’t allow for this, you should use a different type of research design instead.
The way you manipulate the independent variable(s) of your research affects the external validity (and thus, the generalizability) of the results.
You need to decide how widely and finely you want to vary your independent variable(s).
How finely will you vary your independent variable?
You can treat studying as:
You can’t always choose how widely or finely you want to vary your independent variable. In some cases, it’s decided for you because of the topic or variables.
It’s important to take into account the sample size of your study. Usually, the experiment’s statistical power increases as the sample size increases, which means you can be more confident about your results. It’s best to use a sample size calculator to calculate the appropriate sample size.
Then, you need to establish which groups you’ll have for your study. An experiment should always contain at least one experimental group and one control group , but it’s possible to have more experimental groups if you have more levels of treatment (e.g., no studying at all, studying a little, studying a lot).
The control group shows us what the results for the experimental group would have been if we hadn’t manipulated the independent variable.
When you assign your participants to groups, you need to make two decisions:
An experiment is often completely randomized, but in some cases, you need to randomize within blocks (or strata).
When it’s not ethical or practical to use randomization, researchers might opt for a partially-random or non-random design. This is called a quasi-experimental design instead of an experimental design.
If you opt for a between-subjects design , participants only receive one of the possible levels of an experimental manipulation. This design is also called an independent measures design or a classic ANOVA design.
Subjects can be randomly assigned to one condition, or you can use matched pairs to ensure that each treatment group has the same variety of participants in the same proportions. This often happens in social or medical research.
If you opt for a within-subjects design , all participants experience every condition of the experimental manipulation consecutively, and you measure the outcome of each manipulation. This is also known as a repeated measures design.
For within-subjects designs, you often have to use counterbalancing , which is the act of randomizing the order of manipulations among participants. This way, you make sure the order of manipulations does not affect the findings.
Participants are randomly assigned to a level of studying (none, low, high).
For the last step, you have to choose one or multiple data collection methods to measure the outcomes for your dependent variable(s). You need to decide on reliable, valid measurements to reduce the risk of error or bias.
Some variables can be measured with scientific equipment, which typically makes for very reliable outcomes. An example of this is measuring time with a stopwatch. Other variables need to be operationalized before you can measure them.
Your choice of measurement affects the level of your data (nominal, ordinal, interval, or ratio) and, therefore, the types of statistical analysis you can conduct to analyze your data.
There are three main types of experimental designs:
In a between-subjects design , each participant is exposed to only one level of the independent variable. This design is often used in surveys, interviews, and observational studies.
In a within-subjects design , each participant is exposed to multiple levels of the independent variable. This design is often used in laboratory studies, where participants are tested multiple times under different conditions.
In a mixed-subjects design, a combination of between-subjects and within-subjects designs, where participants are tested under different conditions at multiple points in time and then compared to each other.
Some people also make a distinction between factorial design, randomized controlled trials, and a cross-over design.
In a factorial design , two or more independent variables are manipulated simultaneously. This design is often used in laboratory studies, where researchers want to investigate the interaction between multiple variables.
In a randomized controlled trial (RCT) , a type of between-subjects design that involves randomly assigning participants to treatment or control groups. This design is often used in medical and social science research to test the effectiveness of interventions.
In a crossover design , participants are randomly assigned to receive different levels of the independent variable at different times. This design is often used in clinical trials to test the effectiveness of multiple treatments.
Your choice of design depends on the research question and variables. Often, multiple designs are possible.
The four principles of experimental design are:
In experimental design , the two main groups are:
In other words, the control group is used as a baseline to compare with the treatment group, which receives the experimental treatment or intervention.
A within-participant design , also known as a repeated-measures design, is a type of experimental design where the same participants are assigned to multiple groups or conditions. Some advantages of this design are:
It’s important to note that within-participant designs also have some limitations, such as increased risk of order effects (where the order of conditions affects the outcome) and carryover effects (where the effects of one condition persist into another condition).
A pre-experimental design is a simple research process that happens before the actual experimental design takes place. The goal is to obtain preliminary results to gauge whether the financial and time investment of a true experiment will be worth it.
The students are asked to participate in a 30-minute meditation session once a week for 4 weeks. The students’ stress levels are measured before and after the meditation sessions with a standardized questionnaire.
Randomization is a crucial component of experimental design , and it’s important for several reasons:
An experimental design diagram is a visual representation of the research design, showing the relationships among the variables, conditions, and participants. It helps researchers to:
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Contributed by the Heat Transfer Division of ASME for publication in the J ournal of T urbomachinery .
Huang, W., Wang, K., Zeng, F., Chen, W., Zhou, W., Wen, X., Peng, D., and Liu, Y. (June 5, 2024). "Film Cooling Performances Under Various Upstream Roughness Conditions: Experimental Investigations and Similarity Hypothesis." ASME. J. Turbomach . doi: https://doi.org/10.1115/1.4065682
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Roughness caused by deposition, erosion, and additive manufacturing can significantly affect gas turbine efficiency. Previous research has often examined film cooling performance under limited roughness configurations, resulting in inconclusive findings. In this study, film cooling performances under various upstream roughness conditions were investigated to simulate the roughness-affected film cooling performance of the suction side and the endwall. Three roughness heights ( k / D = 0.1, 0.2, and 0.4) and shapes ( k s / k = 0.17, 0.67, and 1.95) were selected to cover a wide range of surface roughness characteristics. Three blowing ratios were examined ( M = 0.5, 1.0, and 1.5). The weakly roughened surfaces showed improved cooling effectiveness as k / D increased. Meanwhile, the moderately and severely roughened surfaces showed a decrease in cooling effectiveness with increasing k / D at M = 0.5 and 1.0 but an increase at M = 1.5. Cases with shallower and higher roughness elements at M = 1.5 outperformed the smooth plate. Subsequently, a similarity hypothesis for film cooling effectiveness was proposed. At all blowing ratios, the scaled cooling effectiveness profiles converged around the smooth plate results for k s / D < 0.391, encompassing common turbine roughness scales, including irregularly roughened surfaces. Deviations emerged at k s / D = 0.782, and they were correlated with the deteriorated regions observed at various blowing ratios. Ensemble-averaged scaled cooling effectiveness exponentially grew with increasing roughness scale for all blowing ratios, and empirical expressions based on smooth plate result and roughness scale were proposed.
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Comparisons of driving characteristics between electric and diesel-powered bus operations along identical bus routes.
3. literature review, 4. data collection.
6. results and findings, 6.1. overall driving patterns, 6.2. differences in overall driving characteristics between electric and diesel buses.
6.2.2. speed–acceleration probability distributions (sapds), 6.2.3. vsp distributions, 6.3. route-based comparison, 6.3.1. route-based driving parameters, 6.3.2. route-based speed–acceleration probability distributions, 6.3.3. route-based vsp distributions, 7. discussions and implications on vehicle energy consumption, 8. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.
Ref. | Location | Main Topic | Objective |
---|---|---|---|
[ ] | Singapore | Vehicle Technology Review | Focus on a comparative analysis between hydrogen and battery-powered buses, includes capital and operating costs, fuel consumption, and fuel cycle emissions. |
[ ] | Canada | Vehicle Technology Review | Conducts a comprehensive review of performance features across three electric bus categories: hybrid, fuel cell, and battery-powered. |
[ ] | Taiwan | Vehicle Technology Review | Performs a multiple attribute evaluation of alternative vehicles. |
[ ] | Germany | Vehicle Technology Review | Provides examples from both domestic and international contexts, illustrating how electric buses contribute to solving energy challenges in modern urban traffic. |
[ ] | China | Vehicle Technology Review | Undertakes a comparative study of two different powertrains for fuel cell hybrid buses. |
[ ] | United States | Vehicle Technology Review | Compares fuel consumption between diesel and hybrid buses under various driving conditions. |
[ ] | Spain | Life Cycle Analysis | Evaluates the overall life cycle of diverse powertrain technologies. |
[ ] | Macau | Life Cycle Analysis | Conducts a comparative life cycle assessment between conventional diesel public buses and electric public buses to assess actual greenhouse gas emissions. |
[ ] | China | Life Cycle Analysis | Assesses the benefits of electric buses compared to their diesel counterparts through a life cycle assessment, considering both upstream fuel production and operation stages. |
[ ] | United States | Life Cycle Analysis | Evaluates the environmental sustainability of electric buses and compares it to diesel buses. |
[ ] | Finland, California | Life Cycle Analysis | Conducts a life cycle cost and carbon dioxide emissions evaluation for different types of city buses. |
[ ] | Germany | Life Cycle Analysis | Compares the environmental footprint of diesel and electric buses across their entire life cycles. |
[ ] | Argentina, Chile, Brazil | Life Cycle Analysis | Carries out a comparative analysis of energy and environmental performances for four types of urban passenger bus powertrains within the well-to-wheel scope. |
Bus Route | Length (km) | Travel Time (mins) | Number of Stops | Origin | Destination | Districts | Circular | Operator |
---|---|---|---|---|---|---|---|---|
7M | 4.5 | 20 | 10 | Lok Fu | Chuk Yuen Estate | Kln | Yes | KMB |
203C | 8.8 | 45 | 25 | Sham Shui Po | Tsim Sha Tsui East | Kln | No | KMB |
11 | 15.7 | 51 | 32 | Central | Jardine’s Lookout | HKI | Yes | CTB |
S65 | 17 | 45 | 21 | Mun Tung Estate | Airport | Island | Yes | LWB |
Bus Type: | Diesel | Electric |
---|---|---|
Supplier: | Alexander Dennis | BYD Auto Industry Company Limited (BYD) |
Model: | Enviro500 | K9R |
Passenger Capacity: | 80 | 66 |
Dimensions: | 11.3 m (L) × 2.5 m (W) × 4.1 m (H) | 11.6 m (L) × 2.5 m (W) × 3.25 m (H) |
Gross Weight: | 19,000 kg | 19,000 kg |
Top Speed: | 90 mph | 62.5 mph |
Motor Type: | Enviro500 | AC Synchronous |
Max Power: | 180 kW | 150 kW |
Max Torque: | 1200 N·m | 550 N·m |
Battery Type: | - | Iron Phosphate |
Battery Capacity: | - | 324 kWh |
Charging Capacity: | - | 80 kW |
Bus Route | Number of Trips (Electric) | Number of Trips (Diesel) | Total Number of Trips |
---|---|---|---|
7M | 45 (33.33%) | 4 (2.97%) | 49 (36.30%) |
203C | 26 (19.26%) | 13 (9.63%) | 39 (28.89%) |
11 | 14 (10.37%) | 6 (4.44%) | 20 (14.81%) |
S65 | 25 (18.52%) | 2 (1.48%) | 27 (20.00%) |
Total | 110 (81.48%) | 25 (18.52%) | 135 (100.00%) |
Abbr. | Name | Unit |
---|---|---|
v | Average speed of the entire driving cycle | km/h |
v | Average running speed | km/h |
a | Average acceleration of all acceleration phases | m/s |
d | Average deceleration of all deceleration phases | m/s |
RMS | Root mean square acceleration | m/s |
PKE | Positive acceleration kinetic energy | m/s |
c | Mean length of a micro-trip | sec |
P | Time proportions of idling modes | % |
P | Time proportions of acceleration modes | % |
P | Time proportions of cruising modes | % |
P | Time proportions of deceleration modes | % |
P | Time proportions of creeping modes | % |
M | Average number of acceleration/deceleration changes (and vice versa) within one micro-trip | number of times |
P | P | P | P | P | RMS | PKE | a | d | v | v | c | M | ||
Overall | Weekday | 33.85 | 27.86 | 7.44 | 29.83 | 1.02 | 0.910 | 0.482 | 0.709 | 0.655 | 13.91 | 20.39 | 44.85 | 12.36 |
Overall | Weekend | 32.52 | 28.13 | 8.73 | 29.82 | 0.80 | 0.866 | 0.450 | 0.671 | 0.629 | 15.04 | 21.62 | 48.11 | 12.87 |
Overall | Off Peak | 37.34 | 26.42 | 6.39 | 28.50 | 1.35 | 0.937 | 0.501 | 0.729 | 0.671 | 12.11 | 18.89 | 37.50 | 10.50 |
Overall | Peak | 32.08 | 28.47 | 7.89 | 30.75 | 0.81 | 0.903 | 0.475 | 0.704 | 0.646 | 14.73 | 21.09 | 48.79 | 13.56 |
Overall | Mean | 33.24 | 27.98 | 8.03 | 29.83 | 0.92 | 0.890 | 0.467 | 0.692 | 0.643 | 14.42 | 20.95 | 46.33 | 12.59 |
P | P | P | P | P | RMS | PKE | a | d | v | v | c | M | ||
Diesel | Weekday | 35.35 | 27.59 | 7.30 | 28.84 | 0.92 | 0.909 | 0.500 | 0.725 | 0.678 | 13.70 | 20.56 | 42.13 | 10.72 |
Diesel | Weekend | 37.41 | 26.44 | 6.99 | 28.03 | 1.13 | 0.835 | 0.450 | 0.658 | 0.622 | 11.08 | 17.65 | 37.05 | 10.17 |
Diesel | Off Peak | 40.79 | 24.91 | 5.45 | 27.74 | 1.11 | 0.986 | 0.541 | 0.783 | 0.702 | 10.93 | 18.45 | 33.79 | 9.31 |
Diesel | Peak | 38.66 | 25.63 | 5.95 | 28.66 | 1.10 | 0.963 | 0.538 | 0.769 | 0.688 | 11.40 | 18.57 | 33.59 | 9.25 |
Diesel | Mean | 36.51 | 26.94 | 7.13 | 28.38 | 1.04 | 0.868 | 0.472 | 0.687 | 0.646 | 12.23 | 18.93 | 39.29 | 10.41 |
P | P | P | P | P | RMS | PKE | a | d | v | v | c | M | ||
Electric | Weekday | 33.58 | 27.91 | 7.46 | 30.00 | 1.04 | 0.910 | 0.479 | 0.706 | 0.651 | 13.94 | 20.37 | 45.34 | 12.66 |
Electric | Weekend | 31.06 | 28.63 | 9.25 | 30.36 | 0.70 | 0.875 | 0.450 | 0.675 | 0.631 | 16.22 | 22.80 | 51.40 | 13.67 |
Electric | Off Peak | 36.68 | 26.71 | 6.57 | 28.64 | 1.40 | 0.928 | 0.493 | 0.719 | 0.665 | 12.34 | 18.97 | 38.22 | 10.73 |
Electric | Peak | 31.35 | 28.78 | 8.10 | 30.99 | 0.78 | 0.897 | 0.468 | 0.697 | 0.641 | 15.10 | 21.37 | 50.48 | 14.04 |
Electric | Mean | 32.49 | 28.22 | 8.23 | 30.16 | 0.89 | 0.895 | 0.466 | 0.693 | 0.642 | 14.92 | 21.42 | 47.95 | 13.09 |
P | P | P | P | P | RMS | PKE | a | d | v | v | c | M | ||
Difference | Weekday | −5.0% | 1.2% | 2.2% | 4.0% | 12.6% | 0.1% | −4.4% | −2.6% | −4.0% | 1.7% | −0.9% | 7.6% | 18.1% |
Difference | Weekend | −17.0% | 8.3% | 32.3% | 8.3% | −38.3% | 4.7% | 0.0% | 2.6% | 1.5% | 46.4% | 29.2% | 38.7% | 34.4% |
Difference | Off Peak | −10.1% | 7.2% | 20.6% | 3.3% | 25.7% | −5.9% | −8.9% | −8.2% | −5.4% | 13.0% | 2.8% | 13.1% | 15.3% |
Difference | Peak | −18.9% | 12.3% | 36.3% | 8.1% | −29.1% | −6.9% | −13.0% | −9.3% | −6.8% | 32.4% | 15.1% | 50.3% | 51.9% |
Difference | Mean | −11.0% | 4.8% | 15.5% | 6.2% | −14.2% | 3.1% | −1.3% | 0.8% | −0.6% | 22.0% | 13.1% | 22.1% | 25.8% |
7M | P | P | P | P | P | RMS | PKE | a | d | v | v | c | M | |
Diesel | Mean | 37.98 | 26.44 | 6.80 | 27.95 | 0.84 | 0.779 | 0.442 | 0.641 | 0.609 | 11.25 | 18.12 | 41.08 | 9.59 |
Electric | Weekday | 31.34 | 28.75 | 7.42 | 31.55 | 0.94 | 0.894 | 0.474 | 0.695 | 0.633 | 12.65 | 18.41 | 40.66 | 11.14 |
Electric | Weekend | 32.63 | 28.12 | 7.81 | 30.85 | 0.60 | 0.904 | 0.471 | 0.701 | 0.638 | 13.14 | 19.51 | 42.66 | 11.57 |
Electric | Mean | 31.86 | 28.49 | 7.58 | 31.27 | 0.80 | 0.898 | 0.473 | 0.698 | 0.635 | 12.85 | 18.86 | 41.48 | 11.32 |
11 | P | P | P | P | P | RMS | PKE | a | d | v | v | c | M | |
Diesel | Mean | 38.39 | 26.33 | 5.35 | 28.14 | 1.80 | 0.998 | 0.559 | 0.788 | 0.738 | 10.96 | 17.75 | 31.35 | 9.31 |
Electric | Weekday | 44.45 | 22.63 | 4.89 | 25.50 | 2.54 | 1.055 | 0.575 | 0.816 | 0.723 | 8.89 | 16.00 | 27.35 | 8.15 |
Electric | Weekend | 39.03 | 25.27 | 5.41 | 28.77 | 1.53 | 1.039 | 0.570 | 0.809 | 0.710 | 10.85 | 17.83 | 28.24 | 8.05 |
Electric | Mean | 42.90 | 23.38 | 5.04 | 26.43 | 2.25 | 1.051 | 0.574 | 0.814 | 0.719 | 9.45 | 16.52 | 27.61 | 8.12 |
203C | P | P | P | P | P | RMS | PKE | a | d | v | v | c | M | |
Diesel | Mean | 38.47 | 25.65 | 6.90 | 28.09 | 0.90 | 0.875 | 0.465 | 0.685 | 0.623 | 11.12 | 18.05 | 36.13 | 10.13 |
Electric | Weekday | 41.09 | 24.78 | 5.93 | 27.47 | 0.74 | 0.930 | 0.494 | 0.722 | 0.650 | 10.91 | 18.40 | 34.04 | 9.13 |
Electric | Weekend | 40.60 | 24.50 | 6.54 | 27.08 | 1.28 | 0.876 | 0.467 | 0.680 | 0.613 | 11.04 | 18.43 | 36.48 | 9.34 |
Electric | Mean | 40.88 | 24.66 | 6.19 | 27.30 | 0.97 | 0.907 | 0.482 | 0.704 | 0.635 | 10.96 | 18.41 | 35.07 | 9.22 |
S65 | P | P | P | P | P | RMS | PKE | a | d | v | v | c | M | |
Diesel | Mean | 15.15 | 38.19 | 14.65 | 31.93 | 0.10 | 0.609 | 0.324 | 0.494 | 0.596 | 25.26 | 29.82 | 80.06 | 17.20 |
Electric | Weekday | 18.79 | 35.02 | 11.98 | 33.91 | 0.30 | 0.789 | 0.381 | 0.609 | 0.629 | 25.74 | 31.65 | 88.15 | 25.14 |
Electric | Weekend | 19.26 | 33.51 | 14.34 | 32.76 | 0.13 | 0.789 | 0.376 | 0.600 | 0.612 | 25.78 | 31.91 | 80.97 | 21.38 |
Electric | Mean | 19.06 | 34.17 | 13.30 | 33.26 | 0.20 | 0.789 | 0.378 | 0.604 | 0.620 | 25.76 | 31.79 | 84.13 | 23.04 |
Section | Metrics | Dataset | Purpose | Results |
---|---|---|---|---|
Driving parameters: a | All Data (Mixed-Route; Mixed bus types) | |||
Driving parameters: b–d; | Electric Only; Diesel Only; (Mixed-Route) | |||
SAPDs a–c and SSD | All Data; Electric Only; Diesel Only; (Mixed-Route) | ). | ||
VSPs | Electric Only; Diesel Only; (Mixed-Route) | |||
Key driving parameters | Route-based data | |||
Route-based Driving Parameters ; | Route-based Electric Only; Route-based Diesel Only | |||
Route-based SAPDs and SSD | Route-based Electric Only; Route-based Diesel Only | , but some routes exhibit a much smaller peak. | ||
Route-based VSP | Route-based Electric Only; Route-based Diesel Only; |
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Ng, K.-W.; Tong, H.-Y. Comparisons of Driving Characteristics between Electric and Diesel-Powered Bus Operations along Identical Bus Routes. Sustainability 2024 , 16 , 4950. https://doi.org/10.3390/su16124950
Ng K-W, Tong H-Y. Comparisons of Driving Characteristics between Electric and Diesel-Powered Bus Operations along Identical Bus Routes. Sustainability . 2024; 16(12):4950. https://doi.org/10.3390/su16124950
Ng, Ka-Wai, and Hing-Yan Tong. 2024. "Comparisons of Driving Characteristics between Electric and Diesel-Powered Bus Operations along Identical Bus Routes" Sustainability 16, no. 12: 4950. https://doi.org/10.3390/su16124950
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scientific hypothesis, an idea that proposes a tentative explanation about a phenomenon or a narrow set of phenomena observed in the natural world.The two primary features of a scientific hypothesis are falsifiability and testability, which are reflected in an "If…then" statement summarizing the idea and in the ability to be supported or refuted through observation and experimentation.
Examples. A research hypothesis, in its plural form "hypotheses," is a specific, testable prediction about the anticipated results of a study, established at its outset. It is a key component of the scientific method. Hypotheses connect theory to data and guide the research process towards expanding scientific understanding.
The hypothesis is the start to the experimental design process, or the process of designing, creating, executing, and analyzing an experiment. Prior to creating the hypothesis, background research ...
Developing a hypothesis (with example) Step 1. Ask a question. Writing a hypothesis begins with a research question that you want to answer. The question should be focused, specific, and researchable within the constraints of your project. Example: Research question.
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 ...
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.
7. Statistical hypothesis. The point of a statistical hypothesis is to test an already existing hypothesis by studying a population sample. Hypothesis like "44% of the Indian population belong in the age group of 22-27." leverage evidence to prove or disprove a particular statement. Characteristics of a Good Hypothesis
Table of contents. Step 1: Define your variables. Step 2: Write your hypothesis. Step 3: Design your experimental treatments. Step 4: Assign your subjects to treatment groups. Step 5: Measure your dependent variable. Other interesting articles. Frequently asked questions about experiments.
Forming a Hypothesis. When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis, and it must be possible to prove your hypothesis true or false.
Experimental science is the queen of sciences and the goal of all speculation. Roger Bacon (1214-1294) Download chapter PDF. Experiments are part of the scientific method that helps to decide the fate of two or more competing hypotheses or explanations on a phenomenon. The term 'experiment' arises from Latin, Experiri, which means, 'to ...
A good experimental design requires a strong understanding of the system you are studying. There are five key steps in designing an experiment: Consider your variables and how they are related. Write a specific, testable hypothesis. Design experimental treatments to manipulate your independent variable.
Experimental Design. Experimental design is a process of planning and conducting scientific experiments to investigate a hypothesis or research question. It involves carefully designing an experiment that can test the hypothesis, and controlling for other variables that may influence the results. Experimental design typically includes ...
Forming a Hypothesis. When conducting scientific experiments, researchers develop hypotheses to guide experimental design. A hypothesis is a suggested explanation that is both testable and falsifiable. You must be able to test your hypothesis, and it must be possible to prove your hypothesis true or false.
The scientific method. At the core of biology and other sciences lies a problem-solving approach called the scientific method. The scientific method has five basic steps, plus one feedback step: Make an observation. Ask a question. Form a hypothesis, or testable explanation. Make a prediction based on the hypothesis.
A hypothesis is generally used if your research topic is fairly well established, and you are relatively certain about the relationship between the variables that will be presented in your research. Since a hypothesis is ideally suited for experimental studies, it will, by its very existence, affect the design of your experiment.
A hypothesis is the process of making careful observations. Learn for free about math, art, computer programming, economics, physics, chemistry, biology, medicine, finance, history, and more. Khan Academy is a nonprofit with the mission of providing a free, world-class education for anyone, anywhere.
Theories and Hypotheses. Before describing how to develop a hypothesis, it is important to distinguish between a theory and a hypothesis. A theory is a coherent explanation or interpretation of one or more phenomena. Although theories can take a variety of forms, one thing they have in common is that they go beyond the phenomena they explain by including variables, structures, processes ...
Developing a hypothesis starts with a clear problem statement. Identify what you want to explore or the issue you aim to resolve. This clarity will shape your entire experiment. Next, gather existing knowledge and data related to your problem. Review relevant studies, articles, or previous experiments.
Characteristics of a Good Research Hypothesis. As the hypothesis is specific, there is a testable prediction about what you expect to happen in a study. You may consider drawing hypothesis from previously published research based on the theory. ... An experimental hypothesis predicts what changes will take place in the dependent variable when ...
An experimental design is a detailed plan for collecting and using data to identify causal relationships. Through careful planning, the design of experiments allows your data collection efforts to have a reasonable chance of detecting effects and testing hypotheses that answer your research questions. An experiment is a data collection ...
The experimental method involves the manipulation of variables to establish cause-and-effect relationships. The key features are controlled methods and the random allocation of participants into controlled and experimental groups. What is an Experiment? An experiment is an investigation in which a hypothesis is scientifically tested. An ...
Hypothesis is a prediction of the outcome of a study. Hypotheses are drawn from theories and research questions or from direct observations. In fact, a research problem can be formulated as a hypothesis. To test the hypothesis we need to formulate it in terms that can actually be analysed with statistical tools.
Functions of Hypothesis. Following are the functions performed by the hypothesis: Hypothesis helps in making an observation and experiments possible. It becomes the start point for the investigation. Hypothesis helps in verifying the observations. It helps in directing the inquiries in the right direction.
Experimental Design | Types, Definition & Examples. Published on June 9, 2024 by Julia Merkus, MA. An experimental design is a systematic plan for conducting an experiment that aims to test a hypothesis or answer a research question.. It involves manipulating one or more independent variables (IVs) and measuring their effect on one or more dependent variables (DVs) while controlling for other ...
Quantitative research design is defined as a research method used in various disciplines, including social sciences, psychology, economics, and market research. It aims to collect and analyze numerical data to answer research questions and test hypotheses. Quantitative research design offers several advantages, including the ability to ...
3 What is an experimental hypothesis? An experimental hypothesis is a tentative explanation of an event or a behavior. It is a statement that predicts the effect of an independent variable on a dependent variable. For example, cognitive behavior therapy (CBT) produces less relapse than antidepressants. The Characteristics of an Experimental ...
Subsequently, a similarity hypothesis for film cooling effectiveness was proposed. At all blowing ratios, the scaled cooling effectiveness profiles converged around the smooth plate results for k s / D < 0.391, encompassing common turbine roughness scales, including irregularly roughened surfaces.
The energy consumption profiles of conventional fuelled and electric vehicles are different due to the fundamental differences in the driving characteristics of these vehicles, which have been actively researched elsewhere but mostly on the basis of uncommon geographical contexts. This study, therefore, collected driving data on electric and conventional diesel buses running along exactly the ...