The most common methodologies in inferential statistics are hypothesis tests, confidence intervals, and regression analysis. Interestingly, these inferential methods can produce similar summary values as descriptive statistics, such as the mean and standard deviation.

The primary difference between descriptive and inferential statistics is that descriptive statistics measure for definitive measurement while inferential statistics note the margin of error of research performed.

There are two main areas of inferential statistics:

• Estimating parameters. This means taking a statistic from your sample data (for example the sample mean) and using it to say something about a population parameter (i.e. the population mean).
• Hypothesis tests.

What are the similarities between descriptive and inferential statistics? Both descriptive and inferential statistics rely on the same set of data. Descriptive statistics rely solely on this set of data, whilst inferential statistics also rely on this data in order to make generalisations about a larger population.

Descriptive statistics characterize the data with which you are working. To generate p-values, assumptions need to be generated. Descriptive statistics do not have p-values. Hypothesis tests, which can test whether or not a descriptive statistic equals a specific value, can have p-values.

The goal in classic inferential statistics is to prove the null hypothesis wrong. What a p-value actually means: The p-value you obtain from a test like this tells you precisely the following: It is the probability that you would obtain these or more extreme results assuming that the null hypothesis is true.

P Values Are NOT the Probability of Making a Mistake The most common mistake is to interpret a P value as the probability of making a mistake by rejecting a true null hypothesis (a Type I error). The null is true but your sample was unusual. The null is false.

P > 0.05 is the probability that the null hypothesis is true. A statistically significant test result (P ≤ 0.05) means that the test hypothesis is false or should be rejected. A P value greater than 0.05 means that no effect was observed.

The P value, or calculated probability, is the probability of finding the observed, or more extreme, results when the null hypothesis (H 0) of a study question is true – the definition of ‘extreme’ depends on how the hypothesis is being tested.

Consider them simply different ways to quantify the “extremeness” of your results under the null hypothesis. The larger the absolute value of the t-value, the smaller the p-value, and the greater the evidence against the null hypothesis.

For example, a p-value of 0.01 would mean there is a 1% chance of committing a Type I error. However, using a lower value for alpha means that you will be less likely to detect a true difference if one really exists (thus risking a type II error).

Terms in this set (26) Can A p-value be negative? P-values correspond to the probability of observing an extreme (or more extreme) event based on the significance level and the assumption that the null hypothesis is true. Since probabilities are NEVER negative, the p-value is NEVER negative.

If your p-value is less than your selected alpha level (typically 0.05), you reject the null hypothesis in favor of the alternative hypothesis. If the p-value is above your alpha value, you fail to reject the null hypothesis.

Being a probability, P can take any value between 0 and 1. Values close to 0 indicate that the observed difference is unlikely to be due to chance, whereas a P value close to 1 suggests no difference between the groups other than due to chance.

A P-value of 0.01 infers, assuming the postulated null hypothesis is correct, any difference seen (or an even bigger “more extreme” difference) in the observed results would occur 1 in 100 (or 1%) of the times a study was repeated. The P-value tells you nothing more than this.

p=0.001 means that the chances are only 1 in a thousand. The choice of significance level at which you reject null hypothesis is arbitrary. Conventionally, 5%, 1% and 0.1% levels are used. Conventionally, p < 0.05 is referred as statistically significant and p < 0.001 as statistically highly significant.