T-Test Paper

From the above output, we can see that the p-value is 0.000186, which is smaller than 0.05 (if we select a 0.05 significance level).

All the p-values are greater than 0.05, therefore there is a statistical difference between each transect.

For d3, t-statistic= 8.8773, t-statistic > t-critical. Thus we reject Ho and d3 is significant.

Data that is statistically significant helps us to understand whether there is a relationship within the null hypothesis. T statistical significant results showed that the participants who consumed caffeine performed worse than participants who did not was less than 5% (p < .05). This means for the author to determine whether a test is significant it must be less than .05, which is the significance level. The P value (probability value) is defined as the probability of obtaining a result equal to or "more extreme" than what was observed (Wikipedia). A p-value helps determine the significance

Reporting t values- when you have already reported p values- is not necessary and can cause confusion.

It tells that the t-statistic with 97 degrees of freedom was 2.14, and the corresponding p-value was less than .05, specifically around 0.035. Therefore, it is appropriate to conclude the research study was statistically significant.

To test the null hypothesis, if the P-Value of the test is less than 0.05 I will reject the null hypothesis.

However, treatment four, 0.1296 (±0.608), represents that the mean was extraneous from what it should be (Table 1). The t-tests show how different the mean is in each treatment.

“Hypothesis testing is a decision-making process for evaluating claims about a population” (Bluman, 2013, p. 398). This process is used to determine if you will accept or reject the hypothesis. The claim is that the bottles contain less than 16 ounces. The null hypothesis is the soda bottles contain 16 ounces. The alternative hypothesis is the bottles contain less than 16 ounces. The significance level will be 0.05. The test method to be used is a t-score. The test statistic is calculated to be -11.24666539 and the P-value is 1.0.  The P-value is the probability of observing a sample statistic as extreme as the test statistic, assuming the null hypothesis is true. The T Crit value is 1.69912702. The calculations show there is enough evidence to support the claim that the soda bottles do

The next table shows the results of this independent t-test. At the .05 significance level, can we conclude

We conduct an independent sample t-test using Excel, and obtain the following output (see sheet T-TEST)

Conclusion : Fails to reject the null hypothesis. The sample does not provide enough evidence to support the claim that mean is significantly different from 12 .

With a P-value of 0.00, we have a strong level of significance. No additional information is needed to ensure that the data given is accurate.

The second step is defining the significance level, determining the degrees of freedom and finding the critical value. The a-level shows that for a result to be statistically significant, it cannot occur more than the a-level percentage of time by chance. The critical value can be obtained by using the t-test table. The degrees of freedom is

The p-value is a measure of the strength of the evidence against the null hypothesis. The p-value is the probability of getting the observed value of the test statistic, or a value with the even greater evidence against Ho, if the null hypothesis is actually true. The smaller the p-value, the greater the evidence against the null hypothesis. If we have a given significance level, then we reject. If we do not have a given significance level, then it is not as cut-and-dried. If the P-value is less than (or equal to) α, then the null hypothesis is rejected in favor of the alternative hypothesis. And, if the P-value is greater than α, then the null hypothesis is not rejected. All statistical tests produce a p-value and this is equal to the probability of obtaining the observed difference, or one more extreme, if the null hypothesis is true. To put it another way if the null hypothesis is true, the p- value is the probability of obtaining a difference at least as large as that observed due to sampling variation. Consequently, if the p-value is small the data support the alternative hypothesis. If the p-value is large the data support the null hypothesis. But how small is ‘small’ and how large is large ‘?! Conventionally a p-value of 0.05 is generally regarded as sufficiently small to reject the null hypothesis. If the p-value is larger than 0.05 we fail to reject the null hypothesis. The 5% value is called the significance level of the test. Other significance levels that are