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Support or Reject Null Hypothesis

However, if see 11 heads, then the P-value is slightly larger than 0.1and the test is not significant.

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Null hypothesis: μ = 72 Alternative hypothesis: μ ≠72

Notice that the top part of the statistic is the difference between the sample mean and the null hypothesis. The bottom part of the calculation is the standard error of the mean.

The p value is a probability, while the f ratio is a test statistic, calculated as:

The null hypothesis can be thought of as a nullifiable hypothesis. That means you can nullify it, or reject it. What happens if you reject the null hypothesis? It gets replaced with the which is what you think might actually be true about a situation. For example, let’s say you think that a certain drug might be responsible for a spate of recent heart attacks. The drug company thinks the drug is safe. The null hypothesis is always the accepted hypothesis; in this example, the drug is on the market, people are using it, and it’s generally accepted to be safe. Therefore, the null hypothesis is that the drug is safe. The alternate hypothesis — the one you want to replace the null hypothesis, is that the drug isn’t safe. Rejecting the null hypothesis in this case means that you will have to prove that the drug is not safe.

Use the following formula to calculate your test value.

Click the link the skip to the situation you need to support or reject null hypothesis for:

Sometimes, you’ll be given a proportion of the population or a percentage and asked to support or reject null hypothesis. In this case you can’t compute a test value by calculating a (you need actual numbers for that), so we use a slightly different technique.

Sample question: A researcher claims that Democrats will win the next election. 4300 voters were polled; 2200 said they would vote Democrat. Decide if you should support or reject null hypothesis. Is there enough evidence at α=0.05 to support this claim?

Use the following formula to calculate your test value.

The following table shows the relationship between power and error in hypothesis testing:

The graph below gives a more complete summary of the statistical relationship between exposure and outcome. The peak of the curve shows the RR=4.2 (the point estimate). In a sense this point at the peak is testing the null hypothesis that the RR=4.2, and the observed data have a point estimate of 4.2, so the data are VERY compatible with this null hypothesis, and the p-value is 1.0. As you move along the horizontal axis, the curve summarizes the statistical relationship between exposure and outcome for an infinite number of hypotheses.

The P-value of the experiment is defined as By convention (note this is arbitrary and therefore a bit controversial), one calls p smaller than0.05 a statistically significant result and a P-value smaller than 0.01 a highly significant result.

A MANOVA analysis generates a p-value that is used to determine whether or not the null hypothesis can be rejected. See  for more information.
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  • Null and Alternative Hypothesis | Real Statistics Using …

    If you are able to reject the null hypothesis in Step 2, you can replace it with the alternate hypothesis.

  • Support or Reject Null Hypothesis in Easy Steps

    Probability of incorrectly rejecting a true null hypothesis

  • Null Hypothesis Definition | Investopedia

    Hypothesis testing - Handbook of Biological Statistics

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P Values (Calculated Probability) and Hypothesis …

Basically, you reject the null hypothesis when your test value falls into the . There are four main ways you’ll compute test values and either support or reject your null hypothesis. Which method you choose depends mainly on if you have a proportion or a .

Null and Alternative Hypothesis | Real Statistics Using Excel

State the null hypothesis. When you state the null hypothesis, you also have to state the alternate hypothesis. Sometimes it is easier to state the alternate hypothesis first, because that’s the researcher’s thoughts about the experiment. (opens in a new window).

One often "rejects the null hypothesis" when the p-value ..

Recall that a sampling distribution of sample means follows a Normal pattern. Most samples will give a result that approximates the population (i.e. true) mean. (The number at the center of the distribution). However, some percentage of the time, by complete fluke, we’ll draw a sample that gives a result much higher or lower than the true mean. These examples (two-tailed tests on left, one-tail tests on right), show that as the likelihood of a sample coming from way out on the sides (i.e. not close to the population value) is smaller, the P value also gets smaller and smaller. We will discuss how to calculate these numbers for P momentarily. (See note).

expect if every single hypothesis were null

Read your first. If the p-value is small (less than your ), you can accept the null hypothesis. Only then should you consider the f-value. If you fail to reject the null, discard the f-value result.

Hypothesis Testing: The Null Hypothesis, Significance, …

The p-value is p = 0.019. This is below the .05 standard, so the result is statistically significant. This means we decide in favor of the alternative hypothesis. We're deciding that the population mean is not 72.

are uniform under the null hypothesis.

The F value in regression is the result of a test where the null hypothesis is that all of the regression coefficients are equal to zero. In other words, the model has no predictive capability. Basically, the f-test compares your model with zero predictor variables (the intercept only model), and decides whether your added coefficients improved the model. If you get a significant result, then whatever coefficients you included in your model improved the model’s fit.

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