• Inference: statistical inference is to perform testing and estimation in such a manner as to provide for the evaluation and control over the probabilities of making such errors.
  
• Tests of hypothesis: the statement about the values of population parameters may or may not be rejected.
  
• Estimation: the process by which sample data are used to indicate the value of an unknown quantity in the population.
  
• Experiment: data from a sample are used to test the validity of the hypothesis is called experiment.
  
• Statistical hypothesis:  the hypothesis is stated in terms of population parameters such as the mean and variance.
  
• Null hypothesis: is a statement about the values of one or more parameters.
  
• Alternative hypothesis:  the statement we hope or suspect is true.
  
• Type I error: null hypothesis is actually true but the decision is to reject it.
  
  Level of significance maximum probability of committing a type I error.
  
  
• Confidence interval: certain probability that the true proportion is within the interval.
  
• Tests of significance: are used to assess the evidence provided by data in support of a claim about the population.
  
• P-value: the probability that the test statistic would take on as extreme a value as that observed, assuming that the null hypothesis is true.
  Sample Mean
   
  

• Standard Deviation 
   
• Propotion p =        
• Standard Error
   
• Comparing proportions
   
• Z Statistic
  

Statistics is the art of decision making in the face of uncertainty. The field of statistical inference consists of those methods used to make decisions or draw conclusions about a population. These methods utilize the information contained in a sample from the population in drawing conclusions.

Tests of significance are used to assess the evidence provided by data in support of a claim about the population. The "strength of the evidence" against the claim is used to refute a false claim. Numerical examples are used to illustrate the steps in the hypothesis testing that leads to the final conclusion.

Specific Tutorial Features:

• The properties of the inference with proportions and means are shown in the examples.
  
• Step by step explanation of hypothesis testing is provided.

Series Features:

• Concept map showing inter-connections of new concepts in this tutorial and those previously introduced.
  
• Definition slides introduce terms as they are needed.
  
• Visual representation of concepts
  
• Animated examples—worked out step by step
  
• A concise summary is given at the conclusion of the tutorial.

• Inference
  
• Test of hypothesis
  
• Type I and type II errors
  
• Inference on the population mean
  
• Confidence interval for proportions
  
• One population proportion hypothesis testing
  
• Comparing two population proportions