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This is the way experimenters and statisticians phrase it, but it's more than a word choice distinction. The null hypothesis is a negative and can not, by definition, be proved.
To test the hypothesis, "A cat runs through my yard at night," we could set up various cat catchers, movement measuring devices, measure the amount of cat food in various locations on the lawn. If we don't find any evidence, we can say, "There's no proof that a cat ran through my yard for however long the experiment lasted." What we do is accept the null hypothesis, "No cat runs through my yard at night." We don't have proof that one didn't because you can't get proof of a negative, but, in the absence of proof that one did, we do not reject the null hypothesis of "No cat."
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Type 1 error and type 2 error?
In statistics: type 1 error is when you reject the null hypothesis but it is actually true. Type 2 is when you fail to reject the null hypothesis but it is actually false. Statistical DecisionTrue State of the Null HypothesisH0 TrueH0 FalseReject H0Type I errorCorrectDo not Reject H0CorrectType II error
What does F equals 1 mean in regards to ANOVA?
F is the test statistic and H0 is the means are equal. A small test statistic such as 1 would mean you would fail to reject the null hypothesis that the means are equal.
Is it true that if you're good at math you will fail physics and chemistry?
I didn't, so the hypothesis is false.
How is algebra related statistics?
The relationship between algebra and statistics may not be immediately apparent. In algebra, you learn how to change an expression from y equals a function of x to x equals a function of y. This ability to transform equations by the rules of algebra is very important in statistics. The standard textbooks in statistics provide equations identifying how to calculate the mean and standard deviation. Generally, from this point, the ability of these statistics based on a limited sample size, to infer (or suggest) properties of the population is introduced. The rules of algebra are used to transform the equation which provides confidence intervals given a sample size to one that provides the sample size given a confidence interval. Similarly, in hypothesis testing, algebra is used again. I can be given a certain level of significance, and decide whether to accept (fail to reject) or reject the null hypothesis. Or, the same equations can be transformed to identify what level of significance is needed to accept the null hypothesis. Algebra is required to understand the relationships between equations. You can think of statistic equations of a series of building blocks, and with algebra you can understand how one equation is derived from another. Not only algebra, but many other areas of mathematics (geometry, trigonometry and calculus) are used in statistics.
Chi-square test of independence?
This tests whether two categorical variables are related, meaning if they affect each (whether they are independent or associated. Your null hypothesis would be that these two variables are independent. Your alternate hypothesis would be that these two variables are dependent. To carry out this test, you must make sure that all the expected counts are greater than 1 and that 80% of these data are greater than 5. Moreover, you must make sure that the data was received by SRS and that the sample is independent. Afterwards, you can plug it in to your graphing calculator in a matrix and use the x^2test. However, if you do not have a graphing calculator, you must calculator the expected value of each value. You do this by multiplying the total counts in the row * total counts in the column/ total counts. Then for each value, you take: (Observed - Expected)^2 / (Expected). After you receive different values, you add them up to make up your x^2. Afterwards, you find the P-Value but looking at a chi distribution curve/table and find the area that is greater than x^2 value. If it is small, you can reject the null hypothesis (like less than 0.05). If not, you fail to reject the null hypothesis and therefore conclude that these two variables are independent.
Can you accept and reject the null Hypothesis Why or why not?
Some people say you can either accept the null hypothesis or reject it. However, there are statisticians that insist you can either reject it or fail to reject it, but you can't accept it because then you're saying it's true. If you fail to reject it, you're only claiming that the data wasn't strong enough to convince you to choose the alternative hypothesis over the null hypothesis.
Difference between acceptance and rejection region?
Some researchers say that a hypothesis test can have one of two outcomes: you accept the null hypothesis or you reject the null hypothesis. Many statisticians, however, take issue with the notion of "accepting the null hypothesis." Instead, they say: you reject the null hypothesis or you fail to reject the null hypothesis. Why the distinction between "acceptance" and "failure to reject?" Acceptance implies that the null hypothesis is true. Failure to reject implies that the data are not sufficiently persuasive for us to prefer the alternative hypothesis over the null hypothesis.
In what cases one can state the null hypotheses true?
The null hypothesis is typically assumed to be true in statistical hypothesis testing. It represents the scenario where there is no significant difference or effect observed between groups or conditions being compared. Researchers seek evidence to reject the null hypothesis in favor of an alternative hypothesis that suggests a real difference or effect exists.
Is scientific investigation expected to be certain?
Absolutely not. Hypothesis testing will never support a hypothesis, only fail to reject it.
What dose it mean if a scientist fail to reject a hypothesis?
It means there is no reason why he should reject it, whether because there is no evidence to the contrary or because an experiment set up to test it affirmed that hypothesis.
Can you not reject null hypothesis?
you do not need to reject a null hypothesis. If you don not that means "we retain the null hypothesis." we retain the null hypothesis when the p-value is large but you have to compare the p-values with alpha levels of .01,.1, and .05 (most common alpha levels). If p-value is above alpha levels then we fail to reject the null hypothesis. retaining the null hypothesis means that we have evidence that something is going to occur (depending on the question)
Type 1 error and type 2 error?
In statistics: type 1 error is when you reject the null hypothesis but it is actually true. Type 2 is when you fail to reject the null hypothesis but it is actually false. Statistical DecisionTrue State of the Null HypothesisH0 TrueH0 FalseReject H0Type I errorCorrectDo not Reject H0CorrectType II error
What is the probability of making a Type II error if the null hypothesis is actually true?
zero. We have a sample from which a statistic is calculated and will challenge our held belief or "status quo" or null hypothesis. Now you present a case where the null hypothesis is true, so the only possible error we could make is to reject the null hypothesis- a type I error. Hypothesis testing generally sets a criteria for the test statistic to reject Ho or fail to reject Ho, so both type 1 and 2 errors are possible.
The results you expect when you test hypothesis?
When testing a hypothesis, you expect to either reject or fail to reject the null hypothesis based on the evidence collected. If the null hypothesis is rejected, it suggests that there is enough evidence to support the alternative hypothesis. If the null hypothesis is not rejected, it implies that the data does not provide enough evidence to support the alternative hypothesis.
Reject Ho or do not reject H0?
Rejecting or Failing to reject the Null Hypothesis (Ho) depends of the P-Value. Generally, the P-value (probability( Observation | Ho ) ) is around .05, thus minimizing the Type 1 error rate. If the P-value < Alpha , you would reject the Ho, and instead believe the Ha (Alternative Hypothesis), and if the P-value > Alpha, you would Fail to reject the Ho because there is not enough evidence to believe the Ha.
When should you accept a null hypothesis?
The null hypothesis cannot be accepted. Statistical tests only check whether differences in means are probably due to chance differences in sampling (the reason variance is so important). So if the p-value obtained by the data is larger than the significance level against which you are testing, we only fail to reject the null. If the p-value is lower than the significance level, the null hypothesis is rejected in favor of the alternative hypothesis.
Does the qualitative observations have to be related to what your hypothesis is?
Original Answer:I would tie it back in and show whether it helped to reject/fail to reject your hypothesis.Different Answer:A hypothesis (Informal definition), is basically a question based on anticipated results. The experiment is created to try to prove or disprove that hypothesis. When conducting an experiment, only three results can occur. That is the hypothesis is confirmed, the hypothesis is incorrect, or the results were inconclusive. Of the three possible answer, the third is the most maddening as it could indicate that something is wrong with your experiment.Sometimes the most fascinating discoveries come from observations that are either inconclusive, or disprove a hypothesis.
