o Three main categories: central tendency, variability, and graphs or tables<\/p>\n

\n
• Inferential statistics: <\/u>a set of statistical procedures used by researchers to test hypotheses about population<\/li>\n
• Distribution: <\/u>a set of scores<\/li>\n
• Central tendency: <\/u>representation of a typical score in a distribution o Three basic measurements used to indicate the central tendency<\/em>: mean, median, and mode o Each of these measurements can yield different values for any given distribution but they all represent a typical score in the distribution<\/li>\n<\/ul>\n

  o Measurements of variability<\/em>: range, standard deviation, and variance<\/p>\n

  \n
  • Variability: <\/u>the spread of scores in a distribution o High variability van occur in a distribution when some participants\u2019 responses differ greatly from other participants\u2019 responses\n
    \n
    • Low variability: thin and tall bell shaped curve o High variability: short and fat bell shaped curve<\/li>\n<\/ul>\n<\/li>\n
    • Mean: <\/u>calculated average of the scores o Most commonly reported measure of central tendency<\/li>\n
    • Median: <\/u>the middle score in a distribution o Reported when outliers are present<\/li>\n
    • Mode: <\/u>the most common score o Often reported when the distribution includes frequencies of responses<\/li>\n
    • Outliers: <\/u>extreme high or low scores in a distribution<\/li>\n
    • Reaction time: <\/u>measurement of the length of time to complete a task<\/li>\n
    • Range: <\/u>the difference between the highest and the lowest scores<\/li>\n
    • Standard Deviation: <\/u>the average difference between the scores AND the mean of the distribution<\/li>\n
    • Variance: <\/u>the standard deviation of a distribution squared<\/li>\n
    • Degrees of freedom: <\/u>number of scores that can vary in the calculation of a statistic o N\u00ad1\n
      \n
      • Used in the calculation of both descriptive and inferential statistics<\/li>\n<\/ul>\n<\/li>\n
      • Frequency distribution: <\/u>a graph of a distribution showing the frequency of each response (how often each score or category appears) in the distribution<\/li>\n
      • Bar graph: <\/u>means for different conditions (bar height represents the size of the mean)<\/li>\n
      • Line graph: <\/u>graph of the means for different conditions in a study where each mean is graphed as a point and the points are connected in a line to show differences between mean scores<\/li>\n
      • Scatterplot: <\/u>shows the relationship between 2 DVs<\/li>\n
      • Predictor variable: <\/u>the DV in a correlational study that is used to predict the score on another variable<\/li>\n
      • Outcome variable: <\/u>the DV in a correlational study that is being predicted by the predictor variable<\/li>\n
      • Scientific\/Alternative hypothesis: <\/u>hypothesis that an effect or relationship exists in the population<\/li>\n
      • Null hypothesis: <\/u>hypothesis that an effect or relationship does not exist in the population o The opposite hypothesis to the scientific or alternative hypothesis<\/li>\n
      • Two\u00adtailed hypothesis: <\/u>both directions of an effect or relationship are considered in the alternative hypothesis of the test<\/li>\n
      • One\u00adtailed hypothesis: <\/u>only one direction of an effect or relationship is predicted in the alternative hypothesis of the test<\/li>\n
      • Distribution of sample means: <\/u>the distribution of all possible sample means for all possible samples from a population<\/li>\n<\/ul>\n

        o Represents the different sample means that can occur when the null hypothesis is true<\/p>\n

        \n
        • Confidence Interval: <\/u>a range of values that the population mean likely falls into with a specific level of certainty<\/li>\n
        • Alpha level: <\/u>probability level used by researchers to indicate the cutoff probability level that allows them to reject the null hypothesis<\/li>\n
        • P\u00advalue: <\/u>probability value associated with an inferential test that indicates the likelihood of obtaining the data in a study when the null hypothesis is true o If this value is less than or equal to alpha, the test is said to be significant<\/li>\n
        • Significant test: <\/u>the p value is less than or equal to alpha in an inferential test, and the null hypothesis can be rejected<\/li>\n
        • Type I Error: <\/u>error made in a significance test when the researcher rejects the null hypothesis when it is actually true<\/li>\n
        • Type II Error: <\/u>error made in a significance test when the researcher fails to reject the null hypothesis<\/li>\n
        • Power: <\/u>ability of a significance test to detect an effect or relationship when one exists o By keeping the Type II error rate low, you are increasing the power of your significance test to detect and effect of relationship that actually exists<\/li>\n<\/ul>\n

           <\/p>\n

          Chapter Summary:<\/p>\n

          \n
          • \n
            \n
            • Data can be summarized with descriptive statistics<\/li>\n
            • Measures of central tendency indicate a typical score in a distribution<\/li>\n
            • Measures of variability indicate the spread of the score in a distribution<\/li>\n
            • Graphs and tables can also provide a visual summary of the data<\/li>\n
            • Inferential statistics estimate sampling error to adjust for how well the sample represents the population in hypothesis testing o An inferential statistic is calculated from the sample values with an estimate of sampling error included in the calculation\n
              \n
              • For each statistic, a p value is determined that indicated the likelihood of obtaining the sample data when the null hypothesis is true.<\/li>\n
              • If the p value is less that or equal to alpha, this is taken as evidence against the null hypothesis about the population, and it can be rejected<\/li>\n
              • Otherwise, the null hypothesis about the population must be retained<\/li>\n<\/ul>\n<\/li>\n
              • Null and alternative hypotheses about populations are stated for studies as either comparisons of conditions or predictions about relationships<\/li>\n
              • <\/li>\n
              • We can determine if there is enough evidence against the null hypothesis to reject it and conclude that the alternative hypothesis is true<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n

                Questions:<\/p>\n

                \n
                1. A __________ hypothesis is a directional hypothesis, whereas a ___________ hypothesis is not<\/li>\n<\/ol>\n

                   Ans: One tailed; two tailed<\/p>\n

                   \n
                   1. Alpha is the highest probability that can be obtained and still __________ the null hypothesis<\/li>\n<\/ol>\n

                      Ans: Reject<\/p>\n

                      \n
                      1. The most common score in a distribution is called the _________<\/li>\n<\/ol>\n

                         Ans: Mode<\/p>\n

                         \n
                         1. An extremely high or low score in a distribution is called a(n) ____________<\/li>\n<\/ol>\n

                            Ans: Outlier<\/p>\n

                            \n
                            1. When scores cover a wide range of values in a data set and differ greatly from one another, the distribution of scores is said to have _______ variability<\/li>\n<\/ol>\n

                               Ans: High<\/p>\n

                               \n
                               1. Inferential statistics provide a probability value about the ______ hypothesis<\/li>\n<\/ol>\n

                                  Ans: Null<\/p>\n","protected":false},"excerpt":{"rendered":"

                                  Descriptive statistics: measures that help us summarize data sets o Summarizes raw data that allows the researchers to get a sense of the data set without reviewing every score… Continue Reading Summarizing and Interpreting Data<\/span><\/a><\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[100],"tags":[],"_links":{"self":[{"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/posts\/3952"}],"collection":[{"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/comments?post=3952"}],"version-history":[{"count":2,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/posts\/3952\/revisions"}],"predecessor-version":[{"id":4740,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/posts\/3952\/revisions\/4740"}],"wp:attachment":[{"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/media?parent=3952"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/categories?post=3952"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.amyork.ca\/academic\/zz\/wp-json\/wp\/v2\/tags?post=3952"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}