Research Data Analysis Lecture - Summary and T-Tests PDF

 is the process of collecting and analyzing large volumes of data in order to identify trends and develop valuable insights. In experimental research, it is used to PROVE OR DISPROVE HYPOTHESES and make predictions and estimations about population. In experimental research, it is used to PR...

 is the process of collecting and analyzing large volumes of data in order to identify trends and develop valuable insights. In experimental research, it is used to PROVE OR DISPROVE HYPOTHESES and make predictions and estimations about population. In experimental research, it is used to PROVE OR DISPROVE HYPOTHESES and make predictions and estimations about population. In parametric In nonparametric statistics, statistics, the the information about the distribution of a population information about the is unknown, and the distribution of the parameters are not fixed, population is known and which makes is necessary to is based on a fixed set test the hypothesis for the of parameters. population.  First, the data need to be normally distributed, which means all data points must follow a bell-shaped curve without any data skewed above or below (Neideen and Brasel, 2007)  The data also need to have equal variance assumed.  Finally, the data need to be continuous.  When to use T-Test? used for evaluating the means of one or two populations using hypothesis testing. T-test can only be used when comparing means of two groups It can be one sample, independent or paired sample T-Test  P-VALUE and ALPHA LEVEL A p value is used in hypothesis testing to help you support or reject the null hypothesis. The p value is the evidence against a null hypothesis. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. Alpha level, also known as the significance level, is the probability of incorrectly rejecting a null hypothesis in a statistical test. It's a crucial factor in hypothesis testing that determines the threshold for statistical significance.  T-VALUE AND T-CRIT A t-value is compared to a t-critical value to determine if a null hypothesis should be rejected: t-value: A value calculated from a sample t-critical value: A value obtained from a t- distribution table The T-Table  Types of T-Test One Sample Independent Sample Unknown Comparing mean of a means of 2 group vs different known mean groups Paired Sample Comparing means of one group at a different time.  One Sample T-Test The one-sample t-test is a statistical hypothesis test used to determine whether an unknown population mean is different from a specific value.  One Sample T-Test A beverage company claims its soda cans contain 12 EX: ounces. A researcher randomly samples their cans and measures the amount of fluid in each one. A one-sample t- test can use the sample data to determine whether the entire population of soda cans differs from the hypothesized value of 12 ounces.  Paired Sample T-Test The Paired Samples t Test compares the means of two measurements taken from the same individual, object, or related units. These "paired" measurements can represent things like: A measurement taken at two different times A measurement taken under two different conditions Measurements taken from two halves or sides of a subject or experimental unit.  Paired Sample T-Test Example: Suppose we want to know whether or not a certain training program is able to increase the max vertical jump (in inches) of college basketball players. To test this, we may recruit a simple random sample of 20 college basketball players and measure each of their max vertical jumps. Then, we may have each player use the training program for one month and then measure their max vertical jump again at the end of the month. Paired Sample T Test  Independent Sample T-Test compares the means of two independent groups in order to determine whether there is statistical evidence that the associated population means are significantly different.  Example A researcher decided to investigate whether an exercise or weight loss intervention is more effe in lowering cholesterol levels. To this end, the researcher recruited a random sample of inactiv males that were classified as overweight. This sample was then randomly split into two groups Group 1 underwent a calorie-controlled diet a Group 2 undertook the exercise-training programme. In order to determine which treatm programme was more effective, the mean cholesterol concentrations were compared betw the two groups at the end of the treatment programmes.