... This is called one-sided because we are
interested only in deviations from the null
hypothesis in one direction.
MIS2502: Data Analytics Descriptive Statistics
... The p-value is a number between 0 and 1.
• A small p-value (typically ≤ 0.05) indicates strong evidence
against the null hypothesis, so you reject the null hypothesis.
• A large p-value (> 0.05) indicates weak evidence against the
null hypothesis, so you fail to reject the null hypothesis.
... that during the summer months on the average 4200 cars pass by the property each day. Being
suspicious that this figure is might be a bit high, the management of the motel chain conducts its
own study and obtains a mean of 4,038 cars per day and a standard deviation of 512 cars per day
for observati ...
... We will begin with a null hypothesis, which states that
there is no difference between the two groups being
A null hypothesis is often times the opposite of what is
expected to happen. We use the null hypothesis so that
we can allow the data to contradict it.
In a randomized controlled exper ...
8.1-8.2 Review Sheet
... 3. The proportion of college graduates who obtain a job after graduation at least 62%.
1.017 Class 10: Common Distributions
... As rejection region grows Type I error increases and Type II error
decreases (test is more likely to reject hypothesis).
As rejection region shrinks Type I error decreases and Type II error
increases (test is less likely to reject hypothesis)
Usual practice is to select rejection region to insure th ...
... we have concluded that the data
are too unlikely to have occurred by
chance alone. Thus, there is a
relationship between the independent
and dependent variable.
Means we have rejected the null
Week 1: Descriptive Statistics
... After 12 weeks, the level of depression in all subjects
was measured and it was found that the mean level of
depression (on a 10-point scale with higher numbers
indicating more depression) was 4 for the
experimental group and 6 for the control group.
The most basic question that can be asked here is ...
Statistical hypothesis testing
A statistical hypothesis is a hypothesis that is testable on the basis of observing a process that is modeled via a set of random variables. A statistical hypothesis test is a method of statistical inference. Commonly, two statistical data sets are compared, or a data set obtained by sampling is compared against a synthetic data set from an idealized model. An hypothesis is proposed for the statistical relationship between the two data sets, and this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. The comparison is deemed statistically significant if the relationship between the data sets would be an unlikely realization of the null hypothesis according to a threshold probability—the significance level. Hypothesis tests are used in determining what outcomes of a study would lead to a rejection of the null hypothesis for a pre-specified level of significance. The process of distinguishing between the null hypothesis and the alternative hypothesis is aided by identifying two conceptual types of errors (type 1 & type 2), and by specifying parametric limits on e.g. how much type 1 error will be permitted.An alternative framework for statistical hypothesis testing is to specify a set of statistical models, one for each candidate hypothesis, and then use model selection techniques to choose the most appropriate model. The most common selection techniques are based on either Akaike information criterion or Bayes factor.Statistical hypothesis testing is sometimes called confirmatory data analysis. It can be contrasted with exploratory data analysis, which may not have pre-specified hypotheses.