The hypothesis is an idea or a premise used as a jumping off the ground for further investigation. It’s essential to scientific research because it serves as a compass for scientists or researchers in carrying out their experiments or studies.
There are different types of hypothesis but crafting a good hypothesis can be tricky. A sound hypothesis should be logical, affirmative, clear, precise, quantifiable or can be tested, and has a cause and effect factor.
Also known as a maintained hypothesis or a research hypothesis, an alternative hypothesis is the exact opposite of a null hypothesis, and it is often used in statistical hypothesis testing. There are four main types of alternative hypothesis:
- Point alternative hypothesis. This hypothesis occurs when the population distribution in the hypothesis test is fully defined and has no unknown parameters. It usually has no practical interest, but it is considered important in other statistical activities.
- Non-directional alternative hypothesis. These hypotheses have nothing to do with either region of rejection (i.e., one-tailed or two-tailed directional hypotheses) but instead, only that the null hypothesis is untrue.
- One-tailed directional hypothesis. This hypothesis is only concerned with the region of direction for one tail of a sampling distribution, not both of them.
- Two-tailed directional hypothesis. This hypothesis is concerned with both regions of rejection of a particular sampling distribution
Known by the symbol H1, this type of hypothesis proclaims the expected relationship between the variables in the theory.
Associative and Causal Hypothesis
Associative hypotheses simply state that there is a relationship between two variables, whereas causal hypotheses state that any difference in the type or amount of one particular variable is going to directly affect the difference in the type or amount of the next variable in the equation. These hypotheses are often used in the field of psychology.
A causal hypothesis looks at how manipulation affects events in the future, while an associative hypothesis looks at how specific events co-occur. A good example of its practical use occurs when discussing the psychological aspects of eyewitness testimonies, and they generally affect four areas of this phenomenon: emotion and memory, system variables in the line-up, estimation of the duration of the event, and own-race bias.
In a complex hypothesis, a relationship exists between the variables. In these hypotheses, there are more than two independent and dependent variables, as demonstrated in the following hypotheses:
- Taking drugs and smoking cigarettes leads to respiratory problems, increased tension, and cancer.
- The people who are older and living in rural areas are happier than people who are younger and who live in the city or suburbs.
- If you eat a high-fat diet and few vegetables, you are more likely to suffer with hypertension and high cholesterol than someone who eats a lot of vegetables and sticks to a low-fat diet.
A directional hypothesis is one regarding either a positive or negative difference or change in the two variables involved. Typically based on aspects such as accepted theory, literature printed on the topic at hand, past research, and even accepted theory, researchers normally develop this type of hypothesis from research questions, and they use statistical methods to check its validity.
Words you often hear in hypotheses that are directional in nature include more, less, increase, decrease, positive, negative, higher, and lower. Directional hypotheses specify the direction or nature of the relationship between two or more independent variables and two or more dependent variables.
This hypothesis states that there is a distinct relationship between two variables; however, it does not predict the exact nature or direction of that particular relationship.
Indicated by the symbol Ho, a null hypothesis predicts that the variables in a certain hypothesis have no relationship to one another, and that the hypothesis is normally subjected to some type of statistical analysis. It essentially states that the data and variables being investigated do not actually exist. A perfect example of this comes when looking at scientific medical studies, where you have both an experimental and control group, and you are hypothesizing that there will be no difference in the results of these two groups.
This hypothesis consists of two variables, an independent variable or cause, and a dependent variable or cause. Simple hypotheses contain a relationship between these two variables. For example, the following are examples of simple hypotheses:
- The more you chew tobacco, the more likely you are to develop mouth cancer.
- The more money you make, the less likely you are to be involved in criminal activity.
- The more educated you are, the more likely you are to have a well-paying job.
This is just a hypothesis that is able to be verified through statistics. It can be either logical or illogical, but if you can use statistics to verify it, it is called a statistical hypothesis.
Facts about Hypotheses
Difference Between Simple and Complex Hypotheses
In a simple hypothesis, there is a dependent and an independent variable, as well as a relationship between the two. The independent variable is the cause and comes first when they’re in chronological order, and the dependent variable describes the effect. In a complex hypothesis, the relationship is between two or more independent variables and two or more dependent variables.
Difference Between Non-Directional and Directional Hypotheses
In a directional research hypothesis, the direction of the relationship is predicted. The advantages of this type of hypothesis include one-tailed statistical tests, theoretical propositions that can be tested in a more precise manner, and the fact that the researcher’s expectations are very clear right from the start.
In a non-directional research hypothesis, the relationship between the variables is predicted but not the direction of that relationship. Reasons to use this type of research hypothesis include when your previous research findings contradict one another and when there is no theory on which to base your predictions.
Difference Between a Hypothesis and a Theory
There are many different differences between a theory and a hypothesis, including the following:
- A hypothesis is a suggestion of what might happen when you test out a theory. It is a prediction of a possible correlation between various phenomena. On the other hand, a theory has been tested and is well-substantiated. If a hypothesis succeeds in proving a certain point, it can then be called a theory.
- The data for a hypothesis is most often very limited, whereas the data relating to a theory has been tested under numerous circumstances.
- A hypothesis offers a very specific instance; that is, it is limited to just one observation. On the other hand, a theory is more generalized and is put through a multitude of experiments and tests, which can then apply to various specific instances.
- The purposes of these two items are different as well. A hypothesis starts with a possibility that is uncertain but can be studied further via observations and experiments. A theory is used to explain why large sets of observations are continuously made.
- Hypotheses are based on various suggestions and possibilities but have uncertain results, while theories have a steady and reliable consensus among scientists and other professionals.
- Both theories and hypotheses are testable and falsifiable, but unlike theories, hypotheses are neither well-tested nor well-substantiated.
What is the Interaction Effect?
This effect describes the two variables’ relationship to one another.
When Writing the Hypothesis, There is a Certain Format to Follow
This includes three aspects:
- The correlational statement
- The comparative statement
- A statistical analysis
How are Hypotheses Used to Test Theories?
- Do not test the entire theory, just the proposition
- It can never be either proved or disproved
When Formulating a Hypothesis, There are Things to Consider
- You have to write it in the present tense
- It has to be empirically testable
- You have to write it in a declarative sentence
- It has to contain all of the variables
- It must contain three parts: the purpose statement, the problem statement, and the research question
- It has to contain the population
What is the Best Definition of a Scientific Hypothesis?
It is essentially an educated guess; however, that guess will lose its credibility if it is falsifiable.
How to Use Research Questions
There are two ways to include research questions when testing a theory. The first is in addition to a hypothesis related to the topic’s other areas of interest, and the second is in place of the actual hypothesis, which occurs in some instances.
Tips to Keep in Mind When Developing a Hypothesis
- Use language that is very precise. Your language should be concise, simple, and clean. This is not a time when you want to be vague, because everything needs to be spelled out in great detail.
- Be as logical as possible. If you believe in something, you want to prove it, and remaining logical at all times is a great start.
- Use research and experimentation to determine whether your hypothesis is testable. All hypotheses need to be proven. You have to know that proving your theory is going to work, even if you find out different in the end.
What is the Number-One Purpose of a Scientific Method?
Scientific methods are there to provide a structured way to get the appropriate evidence in order to either refute or prove a scientific hypothesis.
Glossary of Terms Related to Hypotheses
Bivariate Data: This is data that includes two distinct variables, which are random and usually graphed via a scatter plot.
Categorical Data: These data fit into a tiny number of very discrete categories. They are usually either nominal, or non-ordered, which can include things such as age or country; or they can be ordinal, or ordered, which includes aspects such as hot or cold temperature.
Correlation: This is a measure of how closely two variables are to one another. It measures whether a change in one random variable corresponds to a change in the other random variable. For example, the correlation between smoking and getting lung cancer has been widely studied.
Data: These are the results found from conducting a survey or experiment, or even an observation study of some type.
Dependent Event: If the happening of one event affects the probability of another event occurring also, they are said to be dependent events.
Distribution: The way the probability of a random variable taking a certain value is described is called its distribution. Possible distribution functions include the cumulative, probability density, or probability mass function.
Element: This refers to an object in a certain set, and that object is an element of that set.
Empirical Probability: This refers to the likelihood of an outcome happening, and it is determined by the repeat performance of a particular experiment. You can do this by dividing the number of times that event took place by the number of times you conducted the experiment.
Equality of Sets: If two sets contain the exact same elements, they are considered equal sets. In order to determine if this is so, it can be advantageous to show that each set is contained in the other set.
Equally Likely Outcomes: Refers to outcomes that have the same probability; for example, if you toss a coin there are only two likely outcomes.
Event: This term refers to the subset of a sample space.
Expected Value: This demonstrates the average value of a quantity that is random and which has been observed numerous times in order to duplicate the same results of previous experiments.
Experiment: A scientific process that results in a set of outcomes which is observable. Even selecting a toy from a box of toys can be considered an experiment in this instance.
Experimental Probability: When you estimate how likely something is to occur, this is an experimental probability example. To get this probability, you divide the number of trials that were successful by the total number of the trials that were performed.
Finite Sample Space: These sample spaces have a finite number of outcomes that could possible occur.
Frequency: The frequency is the number of times a certain value occurs when you observe an experiment’s results.
Frequency Distribution: This refers to the data that describes possible groups or values and the frequencies that correspond to those groups or values.
Histogram: A histogram, or frequency histogram, is a bar graph that demonstrates how frequently data points occur.
Independent Event: If two events occur, and one event’s outcome has no effect on the other’s outcome, this is known as an independent event.
Infinite Sample Space: This refers to a sample space that consists of outcomes with an infinite number of possibilities.
Mutually Exclusive: Events are mutually exclusive if their outcomes have absolutely nothing in common.
Notations: Notations are operations or quantities described by symbols instead of numbers.
Observational Study: Like the name implies, these are studies that allow you to collect data through basic observation.
Odds: This is a way to express the likelihood that a certain event will happen. If you see odds of m:n, it means it is expected that a certain event will happen m times for every n times it does not happen.
One-Variable Data: Data that have related behaviors usually associated in some important way.
Outcome: The outcome is simply the result of a particular experiment. If you consider a set of all of the possible outcomes, this is called the sample space.
Probability: A probability is merely the likelihood that a certain event will take place, and it is expressed on a scale of 0 to one, with 0 meaning it is impossible that it will happen and one being a certainty that it will happen. Probability can also be expressed as a percentage, starting with 0 and ending at 100%.
Random Experiment: A random experiment is one whereby the outcome can’t be predicted with any amount of certainty, at least not before the experiment actually takes place.
Random Variable: Random variables take on different numerical values, based on the results of a particular experiment.
Replacement: Replacement is the act of returning or replacing an item back into a sample space, which takes place after an event and allows the item to be chosen more than one time.
Sample Space: This term refers to all of the possible outcomes that could result from a probability experiment.
Set: A collection of objects that is well-defined is called a set.
Simple Event: When an event is a single element of the sample space, it is known as a simple event.
Simulation: A simulation is a type of experiment that mimics a real-life event.
Single-Variable Data: These are data that use only one unknown variable.
Statistics: This is the branch of mathematics that deals with the study of quantitative data. If you analyze certain events that are governed by probability, this is called statistics.
Theoretical Probability: This probability describes the ratio of the number of outcomes in a specific event to the number of outcomes found in the sample space. It is based on the presumption that all outcomes are equally liable.
Union: Usually described by the symbol ∪, or the cup symbol, a union describes the combination of two or more sets and their elements.
Variable: A variable is a quantity that varies and is almost always represented by letters.