INFERENTIAL STATS

• Descriptive statistics


  Allow us to summarize characteristics of the sample
• Inferential statistics


  Also referred to as hypothesis testing, helps us to determine how likely a given outcome is
• Inferential statistics uses sample to draw conclusions about a population, we're never certain that we know the truth about the population
• We can only say that a certain conclusion is likely—or probable
• Developing hypotheses

  1. Plan the collection of data from a sample

  2. Identify the population

  3. Recruit a sample

  4. Choose the independent and dependent variables
• Independent variable


  Presence or absence of the healthy crackers in the photo of the meal
• Dependent variable


  Number of calories estimated
• Control group


  A level of the independent variable that does not receive the treatment of interest in a study
• Experimental group


  A level of the independent variable that receives the treatment or intervention of interest
• Null hypothesis


  A statement that postulates that there is no difference between populations or that the difference is in a direction opposite from that anticipated by the researcher
• Research hypothesis


  Also called the alternative hypothesis, a statement that postulates that there is a difference between populations or sometimes, more specifically, that there is a difference in a certain direction, positive or negative
• Making a decision about our hypothesis

  1. Reject the null hypothesis

  2. Fail to reject the null hypothesis
• We always begin our reasoning about the outcome of an experiment by reminding ourselves that we are testing the (boring) null hypothesis
• In hypothesis testing, we determine the probability that we would see a difference between the means of our samples given that there is no actual difference between the underlying population
• Rejecting the null hypothesis means "I reject the idea that there is no mean difference between populations"
• Failing to reject the null hypothesis means "I do not reject the idea that there is no mean difference between populations"
• To reject the null hypothesis, the group that viewed the photo that included the healthy crackers has a mean calorie estimate that is a good deal higher (or lower) than the control group's mean calorie estimate
• When the data do not suggest a difference, we fail to reject the null hypothesis, which is that there is no mean difference
• The way we decide whether to reject the null hypothesis is based directly on probability
• The null hypothesis is that there is no difference between groups, and usually our hypotheses explore the possibility of a mean difference
• We either reject or fail to reject the null hypothesis. There are no other options
• We never use the word accept in reference to formal hypothesis testing
• People who saw the photo with just the salad and Pepsi estimated, on average, that the 934-calorie meal contained 1011 calories
• When the 100-calorie crackers were added, the meal actually increased from 934 calories to 1034 calories
• Those who viewed this photo estimated, on average, that the meal contained only 835 calories
• Even though the meal with the crackers contained 100 more calories, the participants who viewed this photo estimated that it contained 176 fewer calories
• Tierney referred to this effect as "a health halo that magically subtracted calories from the rest of the meal"