Chapter 14 - Inferential data analysis. This chapter includes contents: Inferential statistics, uses for inferential statistics, sampling error, hypothesis testing, hypothesis testing procedures, statistical significance, parametric statistics, t-tests, types of t-test,. | Chapter 14 Inferential Data Analysis Inferential Statistics Techniques that allow us to study samples and then make generalizations about the population. Inferential statistics are a very crucial part of scientific research in that these techniques are used to test hypotheses Uses for Inferential Statistics Statistics for determining differences between experimental and control groups in experimental research Statistics used in descriptive research when comparisons are made between different groups These statistics enable the researcher to evaluate the effects of an independent variable on a dependent variable Sampling Error Differences between a sample statistic and a population parameter because the sample is not perfectly representative of the population Hypothesis Testing The purpose of the statistical test is to evaluate the null hypothesis (H0) at a specified level of significance (., p < .05) In other words, do the treatment effects differ significantly so that these differences would be attributable to chance occurrence less than 5 times in 100? Hypothesis Testing Procedures State the hypothesis (H0) Select the probability level (alpha) Determine the value needed for significance Calculate the test statistic Accept or reject H0 Statistical Significance A statement in the research literature that the statistical test was significant indicates that the value of the calculated statistic warranted rejection of the null hypothesis For a difference question, this suggests a real difference and not one due to sampling error Parametric Statistics Techniques which require basic assumptions about the data, for example: normality of distribution homogeneity of variance requirement of interval or ratio data Most prevalent in HHP Many statistical techniques are considered robust to violations of the assumptions, meaning that the outcome of the statistical test should still be considered valid t-tests Characteristics of t-tests requires interval or ratio level . | Chapter 14 Inferential Data Analysis Inferential Statistics Techniques that allow us to study samples and then make generalizations about the population. Inferential statistics are a very crucial part of scientific research in that these techniques are used to test hypotheses Uses for Inferential Statistics Statistics for determining differences between experimental and control groups in experimental research Statistics used in descriptive research when comparisons are made between different groups These statistics enable the researcher to evaluate the effects of an independent variable on a dependent variable Sampling Error Differences between a sample statistic and a population parameter because the sample is not perfectly representative of the population Hypothesis Testing The purpose of the statistical test is to evaluate the null hypothesis (H0) at a specified level of significance (., p < .05) In other words, do the treatment effects differ significantly so that these .
