What is a null hypothesis?

In statistics, a null hypothesis (denoted as H0) is a statement that assumes there is no significant difference or relationship between variables being studied. Its purpose is to be tested and either accepted or rejected based on the evidence obtained from a statistical analysis. The null hypothesis is often used in hypothesis testing, where researchers aim to determine if there is enough evidence to support an alternative hypothesis (denoted as Ha) that suggests there is a significant difference or relationship. The null hypothesis, in contrast, assumes that any observed difference or relationship is due to random chance or sampling error. A concrete example of a null hypothesis could be in a study investigating the effect of a new drug on blood pressure. The null hypothesis in this case would state that there is no significant difference in blood pressure between the group of individuals who receive the drug and the group who receive a placebo. The alternative hypothesis would suggest that there is a significant difference in blood pressure between the two groups. To test this hypothesis, researchers would collect data on blood pressure levels from both groups, analyze the data using appropriate statistical tests, and then determine whether there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis.

The null hypothesis in statistics is the assumed probability. For example if you're testing the fairness of a dice, the null hypothesis would be H0: p = ⅙ as that is what is should be if it's fair.

Null Hypothesis (H0) a fundamental concept in Statistics used as a basis for hypothesis testing and states that there is no relationship between the variables studied and any observed difference or effect is due to random chance or variability in data. In hypothesis testing, we compare the result of a statistical analysis with the null hypothesis to determine if there is enough evidence to reject this hypothesis in favour of an alternate one. Eg : Imagine a case of coin toss where you are testing if the coin is fair, ie there is equal chance for a head or tail. Here the null hypothesis is that the coin is fair ie probability of getting head and tail is 0.5; Null Hypothesis H0 : Coin is fair, P(Heads) = 0.5 P (Tails) = 0.5. Now you can perform an experiment of tossing the coin 100 times to gather evidence to test the null hypothesis and prove if the coin toss if fair or biased. To do this a statistical analysis using appropriate methods should be carried out to find the probability and prove if the coin toss is fair and hence to determine if the null hypothesis is to be rejected and an alternate hypothesis formulated.

: A null hypothesis is a statement, in which there is no relationship between two variables. An alternative hypothesis is a statement; that is simply the inverse of the null hypothesis, i.e. there is some statistical significance between two measured phenomenon.

A null hypothesis is what, the researcher tries to disprove whereas an alternative hypothesis is what the researcher wants to prove.

In general it is commonly accepted fact such as earth is round or sky is blue. To prove whether or not the test is supported.

A null hypothesis is theory or statement you are trying to prove. Something like 35 % of my school support Liverpool . You then test this to prove if it’s right

Hi Elliot, A null hypothesis is a statement that there is no significant effect, no difference, or no relationship between variables or phenomena being studied. it represents the default or baseline assumption that any observed differences or effects are due to chance or random variation. Researchers often start by formulating a null hypothesis alongside an alternative hypothesis (also known as the research hypothesis). The alternative hypothesis suggests the presence of a specific effect or relationship, while the null hypothesis suggests the absence of that effect or relationship.

The null hypothesis noted H0 is the standard hypothesis that we want to check: it consists in saying that there is no difference between the compared parameters or that the difference observed is not significant and is due to fluctuations in sampling. For example, the null hypothesis H0 specifies that there is no causal relationship between a new treatment and a reduction in disease symptoms.

The null hypothesis is the assumption that two things are the same or that an explanatory variable has no effect on a response variable. Can you give me more context?

Hi there, The null hypothesis is what you have assumed to be true. For example if I am testing whether or not the mean height of a class has increased from 170cm for example my null would be H0: mu = 170 and my new hypothesis would be mu > 170cm. Same would apply if we’re testing whether the probability of something has changed. Let me know if you need any more help! Ruben

The null hypothesis essentially represents the default assumption that there is no significant effect, difference, or relationship between variables. It's a way to approach a problem with skepticism and to challenge any proposed changes or relationships. For example, let's say a pharmaceutical company develops a new drug and wants to test its effectiveness in treating a specific condition. The null hypothesis in this case might be: "The new drug has no significant effect on the condition compared to a placebo." The purpose of the null hypothesis is to provide a baseline for comparison. Researchers then collect data and perform statistical analysis to determine if there's enough evidence to reject the null hypothesis. If the evidence is strong enough, the null hypothesis is rejected in favour of the alternative hypothesis, which would state the specific effect of the new drug on the condition. In other words, the null hypothesis helps scientists and researchers assess whether their findings are statistically meaningful and not just due to random chance. It's a fundamental concept in hypothesis testing that helps ensure the validity of scientific conclusions.

Of course! Let's say you're testing a new teaching method to see if it improves students' test scores. The null hypothesis would be: "The new teaching method has no effect on students' test scores." You gather data from two groups: one taught using the traditional method and the other using the new method. After analyzing the results, if you find a significant difference in test scores between the two groups, you might reject the null hypothesis and conclude that the new teaching method does have an effect on test scores. If there's no noticeable difference, you might not have enough evidence to reject the null hypothesis, indicating that the new method might not be significantly different from the traditional one in terms of improving test scores.

Imagine you have a super cool idea about something, like, say, eating a special kind of cookie makes you run faster. The null hypothesis is like saying, "Hey, hold on a sec, maybe eating those cookies doesn't really make a difference." It's kind of like being a detective. You start with the idea that there's no connection between the cookie and running speed. This is important because it helps us check if our cool idea is really true or if it's just a coincidence. So, the null hypothesis is like the starting point for our investigation. We do tests and experiments to see if there's enough evidence to say, "Hey, we were right! Eating those cookies does make us run faster!" or if we need to go back to the drawing board. For example, if you and your friends eat those special cookies and you all run just as fast as before, that might support the null hypothesis. But if you and your friends start running faster after eating the cookies, then you might have some evidence to reject the null hypothesis and say your cool idea could be true.

Hi, The null hypothesis in statistics is the assumed probability. For example if you're testing the fairness of a dice, the null hypothesis would be H0: p = ⅙ as that is what is should be if it's fair.

The null hypothesis, in the context of hypothesis tests, is what the probability of an event is currently assumed to be._______________________________________________ For example, we would assume a coin has an equal chance of being flipped heads or tails. We would say The Null Hypothesis = 1/2

Hello Elliot, In statistics, a null hypothesis (denoted as H0) is a statement that assumes there is no significant difference or relationship between variables or that any observed difference is due to chance. It is a hypothesis that is tested and either accepted or rejected based on the evidence provided by collected data. The null hypothesis is often used in hypothesis testing, where researchers aim to investigate whether there is a meaningful difference or relationship between variables in a population. By formulating a null hypothesis, researchers can then collect data and analyze it to determine whether the evidence supports rejecting the null hypothesis in favor of an alternative hypothesis. For example, let's say a researcher wants to test whether a new drug is effective in reducing cholesterol levels. The null hypothesis in this case would be that there is no significant difference in cholesterol levels between the control group (not taking the drug) and the experimental group (taking the drug). The alternative hypothesis would be that there is a significant difference in cholesterol levels between the two groups. Through statistical analysis of the collected data, the researcher can evaluate the likelihood of observing the results under the assumption of the null hypothesis. If the probability of observing the results by chance alone is very low (typically below a predetermined threshold, such as 5% or 1%), the null hypothesis may be rejected in favor of the alternative hypothesis, indicating that there is evidence of a significant difference or relationship. It's important to note that failing to reject the null hypothesis does not necessarily prove that the null hypothesis is true. It simply means that there is not enough evidence to support the alternative hypothesis at the given level of significance.

A null hypothesis is an hypothesis of no difference. For instance, the population of male globally= population of female globally

Null hypothesis is a type of statistical hypothesis that proposed that no statistical significance exist in a set of given observation and example if one plant is watered with distilled water and the other with mineral water then there no difference in growth and nourishment of these two plants

In statistics, the null hypothesis (often denoted H0) is the claim that no relationship exists between two sets of data or variables being analyzed. The null hypothesis is that any experimentally observed difference is due to chance alone, and an underlying causative relationship does not exist, hence the term "null". In other words, the null hypothesis is the assumption that the observed results are due to random chance, and not due to any real effect. The alternative hypothesis is the claim that there is a relationship between the two sets of data or variables. The alternative hypothesis is typically what the researcher is trying to prove. In order to test the null hypothesis, a statistical test is performed. The results of the statistical test will tell us whether or not we can reject the null hypothesis. If we can reject the null hypothesis, then we can conclude that there is a statistically significant difference between the two sets of data or variables. However, if we cannot reject the null hypothesis, then we cannot conclude that there is a difference. We can only say that the evidence is not strong enough to reject the null hypothesis. The null hypothesis is often stated in the negative, such as "there is no difference between the two groups" or "the mean of the population is equal to 0". This is because it is easier to prove that something does not exist than to prove that it does exist. The null hypothesis is an important part of hypothesis testing. It is the starting point for all statistical tests, and it is used to determine whether or not there is enough evidence to support the alternative hypothesis. Here are some examples of null hypotheses: There is no difference in the average height of men and women. There is no relationship between smoking and lung cancer. The average weight of dogs in this breed is 10 pounds. I hope this helps!

The null hypothesis assumes that any kind of difference between the chosen characteristics that you see in a set of data is due to chance.

In statistics, a null hypothesis (often denoted as Ho) is a statement that suggests there is no significant difference, effect, or relationship between the variables being studied. It serves as a starting point for hypothesis testing, where you collect and analyze data to determine if there's enough evidence to reject the null hypothesis in favor of an alternative hypothesis. The null hypothesis is typically a statement of "no effect" or "no difference." Its purpose is to provide a benchmark against which you can compare the results of your statistical analysis. In hypothesis testing, you aim to determine whether the evidence in your data is strong enough to challenge this null hypothesis.

Hi Elliott, A null hypothesis is a statement in statistics that suggests there is no significant difference or relationship between variables being studied. It serves as a basis for comparison to determine whether any observed results are due to chance or if there's a genuine effect. Researchers typically try to reject the null hypothesis in favor of an alternative hypothesis that suggests a meaningful relationship or difference between the variables.

A null hypothesis (often denoted as (Ho) is a statement made in statistical hypothesis testing that suggests there is no significant difference, effect, or relationship between variables. Its purpose is to serve as a starting point for statistical analysis, allowing researchers to assess whether any observed results are due to random chance or if there's evidence to support an alternative hypothesis ((H1). Here's a concrete example to illustrate the concept: **Example: Coin Toss** Let's say you have a coin and you want to test whether it's a fair coin (equally likely to land heads or tails). Your null hypothesis (\(H_0\)) in this case could be that the coin is fair. You would then gather data by repeatedly flipping the coin and recording the outcomes. After collecting a sufficient amount of data, you would perform a statistical test to determine whether the observed results support the null hypothesis or if there's enough evidence to reject it in favor of the alternative hypothesis. If your analysis indicates that the observed results are unlikely to have occurred by chance and there's a significant difference in the frequencies of heads and tails, you might reject the null hypothesis in favor of the alternative hypothesis. Conversely, if the observed results could reasonably occur due to chance, you would fail to reject the null hypothesis. In summary, the null hypothesis is a fundamental concept in hypothesis testing that provides a baseline assumption to compare against observed data. Its purpose is to help determine whether the observed results are statistically significant or merely due to random chance.

null hypothesis is a statement in statistics that suggests there is no significant difference or effect between groups or variables being studied. It serves as a baseline assumption that any observed differences or effects are due to random chance rather than a meaningful relationship. Researchers use hypothesis testing to determine whether the data provides enough evidence to reject the null hypothesis in favor of an alternative hypothesis Here is the example Null Hypothesis : There is no significant difference in test scores between students who receive tutoring and those who do not. Alternative Hypothesis (Ha): Students who receive tutoring have significantly higher test scores compared to those who do not receive tutoring. In this example, the null hypothesis suggests that tutoring doesn't make a difference in test scores, while the alternative hypothesis suggests that tutoring does have an impact on test scores. Researchers collect and analyze data to determine whether there's enough evidence to reject the null hypothesis in favor of the alternative hypothesis.

Null hypothesis is a type of statistical hypothesis that states that no statistical importance functions in a set of given observations. This could also be referred to as a conjecture which is invariably used in quantitative analysis.

The null hypothesis is a fundamental concept in statistics. It represents a statement that there is no significant difference or relationship between variables being studied. It's often denoted as "H0" and serves as a starting point for hypothesis testing. Researchers then gather data to determine whether there's enough evidence to reject the null hypothesis in favor of an alternative hypothesis, which suggests there is a significant difference or relationship.

Null hypothesis is defined as “the commonly accepted fact (such as the sky is blue) and researcher aim to reject or nullify this fact”. More formally, we can define a null hypothesis as “a statistical theory suggesting that no statistical relationship exists between given observed variables”.

Null hypothesis is what is you want to prove for example in the class there is 35% is female and you want to prove it this is null hypothesis

Hi Elliott, Imagine you're trying to find out if a new study method helps students get better grades. The null hypothesis is like saying, "The new study method doesn't really make a difference in grades. Any changes we see could just be due to chance." It's like starting with the idea that there's no real effect. Then, you do experiments and gather data. If the data shows a big difference in grades, you might start to think that the new study method does work. You could reject the null hypothesis and say, "Hey, there's something to this new method!" In simple terms, the null hypothesis is like saying there's no change or effect, and researchers try to gather evidence to see if they can prove that wrong.

The null hypothesis is a statement that suggests that there isn’t a significant difference between groups/conditions in an experiment. For example, let’s say a company is testing out a new medication to see if it’s more effective than a medication that already exists for treating a certain condition, the null hypothesis would be “there is no difference in effectiveness between the new and the existing drug”. However, alternate hypothesis is the opposite and suggests there is a difference between them. We can either reject the null hypothesis or fail to reject it and this is based on the p value you calculate from given data. If the p value is greater than the significant level of 0.05 or 0.01 (usually given in question), then we fail to reject the null hypothesis, meaning that there isn’t a significant difference and results may be due to chance. I hope this made sense, feel free to ask any questions or for me to go through it again if you have any doubts.

What Is a Null Hypothesis? A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations. Hypothesis testing is used to assess the credibility of a hypothesis by using sample data. Sometimes referred to simply as the "null," it is represented as H0. The null hypothesis, also known as the conjecture, is used in quantitative analysis to test theories about markets, investing strategies, or economies to decide if an idea is true or false. A null hypothesis is a type of conjecture in statistics that proposes that there is no difference between certain characteristics of a population or data-generating process. The alternative hypothesis proposes that there is a difference. Hypothesis testing provides a method to reject a null hypothesis within a certain confidence level. If you can reject the null hypothesis, it provides support for the alternative hypothesis. Null hypothesis testing is the basis of the principle of falsification in science. Example of null hypothesis. Education has no effect on mobility.”

In statistics, the null hypothesis (often denoted H0) is the claim that no relationship exists between two sets of data or variables being analyzed. The null hypothesis is that any experimentally observed difference is due to chance alone, and an underlying causative relationship does not exist, hence the term "null". In other words, the null hypothesis states that there is no effect or no difference between the two variables being studied. The purpose of the null hypothesis is to provide a baseline against which to test the alternative hypothesis. The alternative hypothesis is the claim that there is a relationship between the two sets of data or variables being analyzed. The null hypothesis is typically rejected if the data is statistically significant, which means that the probability of the observed results occurring by chance is very low. Here is an example of a null hypothesis: There is no difference in the average test scores of students who took the SAT preparation course and those who did not. Here is an example of an alternative hypothesis: Students who took the SAT preparation course had significantly higher average test scores than those who did not. The null hypothesis is often denoted by H0, and the alternative hypothesis is often denoted by H1. The null hypothesis is not always rejected. If the data is not statistically significant, then the null hypothesis cannot be rejected. This means that there is not enough evidence to support the alternative hypothesis. In statistics, it is important to remember that the null hypothesis is never proven. It can only be rejected. This is because it is impossible to prove that something does not exist.

The null hypothesis assumes that any kind of difference between the chosen characteristics that you see in a set of data is due to chance. For example, if the expected earnings for the gambling game are truly equal to zero, then any difference between the average earnings in the data and zero is due to chance.

Let me start by saying hypothesis is a claim that is proven after completion of a research. Now, a null hypothesis is the claim that there will not be significant effetc on the population/subject matter. Opposite to null hypothesis is caled alternative hypothesis. Welcome fro more info.

A null hypothesis is a statement in a statistical hypothesis test that suggests there is no significant difference or effect between the variables being studied. It serves as a starting point for analysis, and researchers try to gather evidence to either reject or fail to reject the null hypothesis based on their data. In simpler terms, it's a hypothesis that assumes there is no relationship or difference between the groups or conditions being compared.

A null hypothesis, often denoted as \(H_0\), is a fundamental concept in statistical hypothesis testing. It's a statement that there is no significant difference or effect between the variables being studied. In other words, it assumes that any observed differences or effects are due to random chance or variability in the data rather than a true underlying relationship. In hypothesis testing, researchers typically compare a null hypothesis with an alternative hypothesis (\(H_1\) or \(H_a\)), which suggests that there is indeed a significant difference or effect present in the data. The goal is to use statistical methods to determine whether the evidence from the data is strong enough to reject the null hypothesis in favor of the alternative hypothesis. In summary, the null hypothesis is a way to set up a default position that there is no effect or relationship, and statistical analysis helps researchers assess whether the observed data provide enough evidence to reject this default position or not.

Hello Elliot, In statistics, a null hypothesis (often denoted as (Ho)) is a statement that suggests there is no significant or meaningful relationship between the variables being studied. It serves as a default assumption that there is no effect, no difference, or no change, and any observed differences or effects in the data are attributed to random chance or variability. In simpler terms, the null hypothesis represents the idea that any observed differences or patterns in a study are simply due to randomness or chance, rather than being caused by the specific factors being investigated. For example, let's say you're conducting an experiment to determine whether a new drug has an effect on lowering blood pressure. The null hypothesis in this case might be that the drug has no effect on blood pressure. This means that any differences in blood pressure observed between the group that received the drug and the group that received a placebo could be due to random fluctuations rather than the drug itself. Scientists and researchers often set out to test the null hypothesis by collecting data and performing statistical analyses. If the analysis indicates that the observed differences or effects are so large that they are unlikely to have occurred by random chance alone, then researchers might reject the null hypothesis in favor of an alternative hypothesis that suggests there is a significant relationship or effect between the variables. In summary, the null hypothesis is a fundamental concept in statistical hypothesis testing, helping researchers assess the significance of their findings and draw meaningful conclusions from data.

Null hypothesis is the assumption that there is no change,difference between two groups,or no relationship between two variables. Example: The NHS recommends that teenagers aged 14 to 17 years old get atleast 8 hours of sleep per night for proper health and wellness. To test this theory, 42 students were randomly selected from a class and asked how many hours of sleep they get per night.The mean from the sample is x=7.5hours. The null hypothesis here is that average student less than 8hours per night.

A null hypothesis states that there is no significant difference between two variables. What this essentially means is that there is no major effect brought about by one variable which affects the other variable. Its purpose is to provide a baseline for gathering data to show whether the trend caused by variables is significant enough to claim that there is an effect between them. For example, if a research hypothesis states that time spent on a game reduces concentration by reducing marks on a test, the null hypothesis would state that there is no significant difference between concentration and time spent on playing games. This claims that the time spent playing games has no significant effect on marks on a test.

In statistics, a null hypothesis is a statement that assumes there is no significant relationship or difference between variables in a population. It represents the default position or the status quo that is being tested against an alternative hypothesis. The purpose of a null hypothesis is to serve as a basis for statistical hypothesis testing. Hypothesis testing is a method used to make inferences or draw conclusions about a population based on sample data. By formulating a null hypothesis, researchers can objectively evaluate the evidence against it and determine if there is sufficient evidence to reject it in favor of an alternative hypothesis. A concrete example can help illustrate the concept. Let's say a researcher is investigating the effect of a new drug on blood pressure. The null hypothesis in this case might state that the new drug has no effect on blood pressure in the population. The alternative hypothesis, on the other hand, would state that the new drug does have an effect on blood pressure. To test the null hypothesis, the researcher would collect data from a sample of individuals and compare their blood pressure before and after taking the drug. Statistical analysis would then be conducted to determine if the observed difference in blood pressure is statistically significant or if it could have occurred by chance alone. If the statistical analysis provides strong evidence to reject the null hypothesis, the researcher can conclude that there is a significant effect of the drug on blood pressure. Conversely, if the evidence is not strong enough to reject the null hypothesis, the researcher would fail to find sufficient evidence to support the claim that the drug has an effect on blood pressure. It's important to note that failing to reject the null hypothesis does not prove that the null hypothesis is true; it simply means that there is not enough evidence to suggest otherwise based on the

The null hypothesis, often denoted as "H0," represents the idea that there is no significant difference or effect, no relationship, or no change in a particular situation or population. Null Hypothesis (H0) This is the hypothesis of no effect or no difference. It states that any observed results are not statistically significant and can be attributed to random chance. Hypothesis testing is a fundamental tool in scientific research and helps ensure that conclusions are drawn based on sound statistical reasoning.

A null hypothesis is when the independent variable doesn't change, or have an effect on the dependent variable. For example: "the amount of sleep you have will not change your performance in exams". This is the opposite of an alternative hypothesis, where the dependent variable does change: "the more sleep you have, the better you will perform in exams".

The null hypothesis is the type of statistical hypothesis that claim that there is no statistical significance (no effects)exit in the set of given observation. It is use to assess the credibility of hypothesis using a simple data. example: the amount of text highlighted in textbook has no effect on exam scores.

The null hypothesis assumes that any kind of difference between the chosen characteristics that you see in a set of data is due to chance. For example, if the expected earnings for the gambling game are truly equal to zero, then any difference between the average earnings in the data and zero is due to chance.

Of course! Imagine you're playing a game where you have a special coin, and you want to see if this coin is fair or if it always lands on heads. Your guess at the start is called the "null hypothesis," which is like saying, "Maybe this coin is fair and doesn't always land on heads." The other idea you want to test is called the "alternative hypothesis," which is like saying, "Actually, this coin might not be fair, and it could be more likely to land on heads." To figure out if your special coin is fair or not, you gather a bunch of your friends and you all start flipping the coin. If you see that the coin lands on heads a lot more than you'd expect by chance, you might start thinking that your null hypothesis (the fair coin idea) isn't right. You might decide to believe the alternative hypothesis instead. But if you see the coin landing on heads about as often as you'd expect from a fair coin, then you might stick with your original guess – the null hypothesis – and say there's no strong reason to believe the coin isn't fair. In short, the null hypothesis is like your first guess, and you use it to check if something you're testing is really true or if it might just be happening by chance.

In simplistic terms your null hypothesis is the old probability. It’s what you’d expect it to be or what it’s always been so with a coin toss h0:p=0.5 and any other probability would have to be an alternative to this(alternative hypothesis). You then have to test you’re new evidence against the old probability to see if such an event could occur under the null hypothesis or if a change has occurred.

A general statement, or default orientation, says that there is no relationship between anything measured, or that there is no dependence between groups. The null hypothesis is usually represented by the symbol H0.

Hi Elliot, A null hypothesis means that no relationship (in statistical terms between phenomena or populations. It's purpose is to allow researchers to work out the likelihood that any differences found between groups or sets of data are due to chance, rather than a true effect. It also allows researchers to conclude as to whether it is more effective to use a null hypothesis than an alternative hypothesis. An example would be that teenagers and adults use mobile devices the same amount of time. Hope this helps further. Kind Regards Ayaz Sheikh

null hypothesis term basically use in statistics , where we chek acceptance of any concent. for example your prepration for exam is upto mark or not. we suppose Ho: Exam prepration is up to mark as set your standard Hi: Exam prepration is not up to mark Test: Solve diffrent question from diffrent topics. If you are able to do more questions of diffrent topic then assess as more than 50 percent or less. Conclusion : at the end conclude which hypo is acceptable.

Hi Elliot,The null hypothesis in statistics is the assumed probability. For example if you're testing the fairness of a dice, the null hypothesis would be H0: p = ⅙ as that is what is should be if it's fair. Let's say you're testing a new teaching method to see if it improves students' test scores. The null hypothesis would be: "The new teaching method has no effect on students' test scores." You gather data from two groups: one taught using the traditional method and the other using the new method. After analyzing the results, if you find a significant difference in test scores between the two groups, you might reject the null hypothesis and conclude that the new teaching method does have an effect on test scores. If there's no noticeable difference, you might not have enough evidence to reject the null hypothesis, indicating that the new method might not be significantly different from the traditional one in terms of improving test scores.

The null hypothesis is that the population parameter is equal to some number. The null hypothesis is assumed to be true unless it is disproven by the test statistic. The null hypothesis is similar to assuming a person is innocent in a court of law, until proven otherwise. When the null hypothesis is rejected, you accept the alternative hypothesis. Rejection of the null hypothesis occurs when the value of the test statistic is larger than a specified value

Hi friend, A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations. Hypothesis testing is used to assess the credibility of a hypothesis by using sample data. Sometimes referred to simply as the "null," it is represented as H0. The null hypothesis, also known as the conjecture, is used in quantitative analysis to test theories about markets, investing strategies, or economies to decide if an idea is true or false

Null hypothesis is a statistical hypothesis that provide a method of rejection, represent by Ho and stated that there is no significant relationship between certain characteristics of population .

With hypothesis tests, we are testing whether something is true or not. Assuming we are testing whether a car will drive for 100 miles, we would have H0- It won't H1- It will

The null hypothesis is the claim that there’s no effect in the population. If the sample provides enough evidence against the claim that there’s no effect in the population (p ≤ α), then we can reject the null hypothesis. Otherwise, we fail to reject the null hypothesis. Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept. Be careful not to say you “prove” or “accept” the null hypothesis.

(in a statistical test) the hypothesis that there is no significant difference between specified populations, any observed difference being due to sampling or experimental error.

The null hypothesis is a statistical statement that claims there is no significant difference or effect between the groups or circumstances being compared. Scientists use data and statistical tests to determine if the observed results are unlikely to have occurred by chance under the null hypothesis. If scientists discover that the data are highly unlikely, they may reject the null hypothesis in favor of a new thought or hypothesis.

Hi Elliot, Null hypothesis is a statement which conveys that there is no difference between a theoritical value and an observed or experimental value. It can also be interpreted as the default condition of an experiment. For example in the case of tossing the coin, with the assumption that its a fair coin, the null hypothesis is : "The probability of getting a tail is 0.5".

The hypothesis that is being tested is called the Null hypothesis. Null hypotheses are actually stated in opposite terms of what is believed to be true with the hope that the test procedure will lead to their rejection.

The Null hypothesis is a type of statistical hypothesis that there is no significant effect or relationship between 2 or more variables in a study or experiment. The null hypothesis is something that you are going out to disprove, this means we need to nullify it/ invalidate it. Example: No statistical relationship exists between plant growth and the different forms of water used in watering the plants eg. Distilled water vs Mineral water. So in this hypothesis there are no statistically significant relationships among the two variables (eg. Distilled water vs mineral water), the hypothesis is said to be a null hypothesis. In the water example – there are no statistical relationships among the types of water given to plants for growth and nourishment.

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations. Hypothesis testing is used to assess the credibility of a hypothesis by using sample data. Null hypothesis is defined as “the commonly accepted fact (such as the sky is blue) and researcher aim to reject or nullify this fact”. More formally, we can define a null hypothesis as “a statistical theory suggesting that no statistical relationship exists between given observed variables”.

In statistics, null hypothesis which is usually represented by H0, means the assumption that there is no difference between two variables (given and observed variables). For example, you measure the mean score of Maths exams between male and female students. Then, the null hypothesis is there is no difference between the mean score of Maths exams of male and female students. The null hypothesis is usually the hypothesis that the researchers want to reject.

Null Hypothesis: In statistical hypothesis testing, the null hypothesis (often denoted as "H0") is a statement that there is no significant difference or effect in a given situation. It is a default assumption that there is no relationship between variables or that any observed effect is due to random chance. For example, if you were conducting an experiment to test the effectiveness of a new drug, the null hypothesis might be that the drug has no effect on the condition being treated. Researchers then gather data and perform statistical tests to determine whether the evidence supports rejecting the null hypothesis in favor of an alternative hypothesis that suggests a significant effect or relationship. Rejecting the null hypothesis typically indicates that there is enough evidence to support a different conclusion. However, failing to reject the null hypothesis doesn't necessarily mean the null hypothesis is true; it simply means that there isn't enough evidence to support an alternative claim at that time.

Hypothesis testing is used to assess the credibility of a hypothesis by using sample data.

A null hypothesis (Ho) is any statement we want to test. For example, the ABC restaurant believes that customers spend no more than £18 per head, on average. The null hypothesis (Ho) would be that x is less than or equal to £18 as the statement says customers spend no more than £18 per head on average. Whereas, the alternate hypothesis (Ha/H1) would be the rejection of the null hypothesis meaning x is more than £18 meaning customers do spend more than £18 per head on average. I hope this helps, let me know if you’d like me to further explain this :)

A null hypothesis states that there is no significant difference or effect between groups or variables being studied.

A null hypothesis, labeled as "H0," is a statement that asserts no significant difference or relationship between variables in a research study. It acts as a starting point to be tested against an alternative hypothesis. Statistical methods are used to determine if the evidence supports rejecting the null hypothesis in favor of the alternative, indicating a meaningful difference or effect. This concept is crucial in hypothesis testing and statistical analysis to draw informed conclusions from research data.

In layman's terms, the null hypothesis is a statement which you'll test, saying that there's nothing interesting happening in the population. Null = nothing interesting to see, move along please! We are only looking at a sample of the population though, so there's a chance we get it wrong. When testing, we pick a confidence level which is essentially the risk we're happy to take that we will be wrong if we test the null hypothesis, and conclude (from our data) that there's an interesting population result, when actually there isn't. See also: Type 1 error, p-value. There are lots of possible examples of hypothesis tests, and it depends on the question you want to answer as well as the type of data available, as to which specific test we will use. But as a really simple example, consider this: I have a theory that taller people have bigger feet, so I stop people in the street and measure their height and shoe size. To test my theory my null hypothesis is: H_0: There's no link between height and shoe size. In this example, my alternative hypothesis (the theory I'm testing), H_1 (or H_A): Taller people have bigger feet (or equivalently, shorter people have smaller feet). We now need to frame this in a logical way which we can test. In this example we're saying there is positive correlation between height and shoe size: as one goes up, so does the other. An appropriate statistical test in this case would be: H0: rho (Pearsons correlation coefficient) = 0 H1: rho >0 Sig level (5% or 0.05) (You can be more strict or lenient here - it's your test after all!) IRL you'd then calculate the correlation coefficient and see if it was as expected if H0 were true. If it is, then we stick with it ("Fail to reject H0".). If the data shows enough evidence to the contrary though, then we would reject H0 and conclude that taller people have bigger feet. Don't forget though, the results of a statistical test apply to populations, and depend upon you doing the right test on good data in the first place! You'll still get outliers - for example, I'm only 165cms tall and have massive feet: EU42/UK8.5

Null hypothesis means there is no relationship between two variables. Its purpose is to state that the variance between two items is zero. It is used calculate to calculate the extent to which an item changes if another changes. This is called the standard deviation. E.g Variance = 0 A--------B Standard deviation = 0

A null hypothesis is a type of statistical hypothesis that proposes that no statistical significance exists in a set of given observations Eg. 1<2>1

Hi Elliot! A null hypothesis, often denoted as "H0," is a fundamental concept in statistics and hypothesis testing. It represents a statement of no effect, no difference, or no association between variables. In essence, it is a hypothesis that there is no significant or meaningful relationship or effect in a given situation. The primary purpose of a null hypothesis is to serve as a reference point for statistical hypothesis testing. In hypothesis testing, we typically have a null hypothesis (H0) and an alternative hypothesis (Ha or H1). These two hypotheses are used to make inferences about population parameters based on sample data. Here's a concrete example to illustrate the concept: Example: Suppose a pharmaceutical company has developed a new drug, Drug X, and wants to determine if it is effective in reducing blood pressure. The null and alternative hypotheses in this case could be formulated as follows: Null Hypothesis (H0): The new drug, Drug X, has no effect on reducing blood pressure. In mathematical terms, this might be expressed as "μX = μ0," where μX represents the mean blood pressure with the drug, and μ0 represents the mean blood pressure without the drug (i.e., no effect). Alternative Hypothesis (Ha or H1): The new drug, Drug X, is effective in reducing blood pressure. In mathematical terms, this could be expressed as "μX < μ0" (indicating a decrease in blood pressure), "μX > μ0" (indicating an increase), or "μX ≠ μ0" (indicating any difference). In this example, the null hypothesis serves as the default assumption that the new drug has no effect. The purpose of hypothesis testing is to assess whether the sample data provides enough evidence to reject this null hypothesis in favor of the alternative hypothesis. If the evidence is strong enough, the null hypothesis is rejected, suggesting that the drug does have an effect. In practice, statistical tests are used to calculate p-values, which indicate the probability of obtaining the observed results if the null hypothesis were true. A small p-value (typically below a predefined significance level, such as 0.05) provides evidence to reject the null hypothesis, suggesting that there is a significant effect or relationship in the population. So, the null hypothesis essentially sets up a scenario where we assume no effect or no difference, and the goal of hypothesis testing is to determine whether the evidence from our sample supports that assumption or suggests otherwise. Feel free to get in touch if you would like me to explain this in a different way or you would find it useful for me to give you another example :)

A null hypothesis is a statement that there is no significant difference or effect between the variables being tested in a scientific experiment. It serves as a baseline assumption to be tested against an alternative hypothesis that suggests a specific effect or relationship. The purpose of the null hypothesis is to allow researchers to determine if any observed differences or effects in their data are statistically significant or if they could have occurred due to random chance. For example, imagine a study investigating the effect of a new drug on blood pressure. The null hypothesis might state that the drug has no effect on blood pressure, while the alternative hypothesis suggests that the drug does have an effect. Researchers would collect data and perform statistical analysis to determine whether the observed changes in blood pressure are significant enough to reject the null hypothesis in favor of the alternative hypothesis.

The null hypothesis is a statement that assumes there is no significant relationship or difference between variables. The purpose of the null hypothesis is to serve as a starting point for statistical analysis and hypothesis testing. It helps determine if there is enough evidence to reject or accept the null hypothesis in favor of an alternative hypothesis.

The null hypothesis, often denoted as "H0," is a fundamental concept in statistical hypothesis testing. It represents the idea that there is no significant difference or effect. In other words, any observed differences are due to chance. When conducting experiments or tests, we compare the null hypothesis to an alternative hypothesis to draw conclusions about the data. Feel free to reach out to me with any more questions you have. I'm here to provide guidance, explanations, and support on these and any other topics you're curious about. Let's work together to excel in your learning journey!

The null hypothesis is the claim that there’s no effect in the population. If the sample provides enough evidence against the claim that there’s no effect in the population (p ≤ α), then we can reject the null hypothesis. Otherwise, we fail to reject the null hypothesis. Although “fail to reject” may sound awkward, it’s the only wording that statisticians accept. Be careful not to say you “prove” or “accept” the null hypothesis.

A null hypothesis is a statement that says there's no significant difference or effect. For example, if you're testing a new drug, the null hypothesis might be that the drug has no effect on patients' recovery. You collect data, analyze it statistically, and if the evidence is strong enough, you can reject the null hypothesis in favor of the idea that the drug does have an effect. It's a way to scientifically confirm or refute ideas using data.

Sure thing! Imagine you're in a classroom, and you hear a rumor that girls are better at math than boys. That's an interesting idea, right? But you want to be a super-scientific detective and find out if it's true. Now, here's where the "null hypothesis" comes in. It's like the detective's starting point, and it says, "Hey, there's no real difference between girls and boys in math skills." In other words, it's like assuming they're both equally good at math until proven otherwise. So, you decide to do a fun experiment. You gather a bunch of girls and boys, give them a math test, and see how they do. After all the math problems are solved and the dust settles, you look at the results. If you find that the girls and boys scored about the same on the math test, that's kind of like saying, "Hey, the null hypothesis wins! There's no big difference." But if, by some chance, you discover that the girls scored way better than the boys on average, you might start to think, "Hmm, maybe there's something to this idea that girls are better at math." So, in a nutshell, the null hypothesis is like starting with a fair, blank slate. You assume there's no big difference until your detective work (or your experiment) shows you otherwise. It's a way scientists and researchers figure out if something they've heard is true or not, all while having fun with math and science! let's explore some more fun examples: 1. Cookie Taste Test: Imagine you're in a heated debate with your friends about whether chocolate chip cookies from Bakery A are better than Bakery B. You decide to do a cookie taste test. The null hypothesis here would be that "there's no real difference in the taste of cookies from Bakery A and Bakery B." If, after tasting, you can't tell them apart, your null hypothesis wins. But if you taste a big difference, you might have to reconsider your hypothesis. 2. Plant Growth Experiment: Let's say you're curious about whether plants grow better with classical music playing. Your null hypothesis could be "playing classical music doesn't make a difference in plant growth." If, after a few weeks of testing, the plants with music and the ones without all grow about the same, you'd stick with your null hypothesis. But if the music plants grow taller and greener, you might think, "Hmm, there might be something to this plant serenade." 3. Coin Flipping Game: You're playing a game where your friend claims they have a magic coin that always lands on heads. Your null hypothesis would be "the coin is just a regular coin, and it has a 50/50 chance of landing on heads or tails." If, after flipping it many times, it lands on heads about half the time, you'd say, "Aha! Null hypothesis is right, it's just a regular coin." So, whether you're talking about cookies, plants, or magic coins, the null hypothesis is like your starting point, saying there's no big difference until you have some evidence that suggests otherwise. It's a detective tool that helps you get to the truth!

Hello Elliot . A null hypothesis (Ho) is a statistical presumption which states that there is no difference or relationship between groups or variables. It is a statement of no change or status quo. The tester believes that there is no difference or relationship between the variables, and if at all there is anyone, it is very insignificant. The purpose of null hypothesis is to guide the researcher or tester in his or her computation and analysis of data , presuming that the variables have no difference or relationship. Concrete example is for the researcher to presume and state that there is no relationship between body weight and speed in movement.

Hi Elliot, I believe I might be able to help. The null hypothesis is denying any difference essentially. Whereas the alternative hypothesis will find a comparison to differentiate different sample/subject groups. Null Hypothesis: H0: There is no difference in the salary of factory workers based on gender. Alternative Hypothesis: Ha: Male factory workers have a higher salary than female factory workers.

Imagine a pharmaceutical company has developed a new drug that they claim can lower cholesterol levels in patients. They want to test whether the drug is indeed effective. Here's how the null hypothesis applies in this scenario: Null Hypothesis :The new drug has no effect on lowering cholesterol levels in patients. Alternative Hypothesis :The new drug does have an effect on lowering cholesterol levels in patients. In this context, the null hypothesis suggests that the new drug doesn't do anything; it's as if the drug has no impact. The alternative hypothesis suggests that the drug does have an effect. Purpose of the Null Hypothesis: The primary purpose of the null hypothesis is to provide a baseline assumption that can be tested statistically. Researchers collect data from a sample of patients and analyze it to determine whether there is enough evidence to reject the null hypothesis in favor of the alternative hypothesis. Here's how the process works: (1)Researchers collect cholesterol level data from a sample of patients before and after administering the new drug. (2)They perform statistical analysis to compare the average cholesterol levels before and after the drug was given. (3)If the data shows a significant decrease in cholesterol levels, and this decrease is unlikely to occur due to random chance, then researchers might reject the null hypothesis and conclude that the drug is effective in lowering cholesterol. In this case, rejecting the null hypothesis would mean that the researchers have evidence to suggest that the drug does indeed have an effect. Remember, the null hypothesis serves as a starting point for hypothesis testing, helping researchers make informed decisions based on evidence and statistical analysis.

this is a claim that is being tested to see if its correct

The null hypothesis is a statement in statistics that suggests there is no significant difference or effect between the variables being studied. It serves as a starting point for hypothesis testing and is usually denoted as "H0". Researchers aim to either reject the null hypothesis in favor of an alternative hypothesis or fail to reject it based on their analysis of data. Null Hypothesis in Statistics Default (GPT-3.5) User What is null hypothesis ChatGPT The null hypothesis is a statement in statistics that suggests there is no significant difference or effect between the variables being studied. It serves as a starting point for hypothesis testing and is usually denoted as "H0". Researchers aim to either reject the null hypothesis in favor of an alternative hypothesis or fail to reject it based on their analysis of data. For example; Let's say a pharmaceutical company develops a new drug they believe is effective in reducing blood pressure. Here's how the null hypothesis might be formulated: Null Hypothesis (H0): The new drug has no effect on reducing blood pressure compared to a placebo. In this case, the null hypothesis suggests that there is no difference in blood pressure reduction between the new drug and a placebo. Researchers would then conduct experiments and gather data to either support this null hypothesis or find evidence against it, leading to the formulation of an alternative hypothesis if the data suggests the drug does indeed have an effect.

the hypothesis that there is no significant difference between specified populations, any observed difference being due to sampling or experimental error

In the field of mathematics and statistics: **Null Hypothesis:** The null hypothesis is a statement that there is no significant difference or effect in a given situation. It suggests that any observed differences are due to random chance. **Purpose:** The purpose of the null hypothesis is to provide a baseline for comparison. It allows us to assess whether the data we observe is statistically significant or if it could have occurred by random variations alone. **Example:** In a coin toss experiment, the null hypothesis could be that the coin is fair (equally likely to land heads or tails). If after many coin tosses, we find that the coin lands heads significantly more often, we might reject the null hypothesis and conclude that the coin is not fair.