Psychological-Statistics-Reviewer.docx

### **PSYCHOLOGICAL STATISTICS REVIEWER FOR ARABELLA C. ABABA** BY: Jimwell pogi ### **Reviewer: Psychological Statistics** **1. What** **is statistics and why is it important in psychology?** - **Statistics:** The branch of mathematics dealing with the collection, analysis, interpretation...

### **PSYCHOLOGICAL STATISTICS REVIEWER FOR ARABELLA C. ABABA** BY: Jimwell pogi ### **Reviewer: Psychological Statistics** **1. What** **is statistics and why is it important in psychology?** - **Statistics:** The branch of mathematics dealing with the collection, analysis, interpretation, and presentation of numerical data. - **Importance in psychology:** Allows psychologists to quantify observations, test hypotheses rigorously, and draw reliable conclusions about human behavior based on empirical evidence. **2. Define population, sample, and sampling methods used in psychological research.** - **Population:** The entire group of individuals or items of interest to a researcher. - **Sample:** A subset of the population selected to represent the larger group. - **Sampling methods:** Techniques such as random sampling, stratified sampling, and convenience sampling used to select samples that accurately reflect the population. **3. Describe the different types of variables used in psychological research.** - **Variables:** Characteristics that can vary or change. - **Types:** - **Independent variable (IV):** Manipulated by the researcher to observe its effect on the dependent variable. - **Dependent variable (DV):** The outcome variable that is measured to assess the effect of the independent variable. - **Control variables:** Variables held constant to prevent them from influencing the results. **4. Explain measures of central tendency (mean, median, mode) and their formulas.** - **Mean:** Average value of a set of numbers. Formula: ** - **Median:** Middle value in a sorted list of numbers. - **Mode:** Most frequently occurring value in a data set. **5. Describe measures of variability (range, variance, standard deviation) and their formulas.** - **Range:** Difference between the maximum and minimum values in a dataset. - **Variance:** Average of the squared differences from the mean. Formula: ** - **Standard deviation:** Square root of the variance. Formula: ** **6. Explain the concept of correlation and its coefficient (r).** - **Correlation:** Measures the strength and direction of a linear relationship between two variables. - **Correlation coefficient (r):** Ranges from -1 to +1. ** - Positive values indicate a positive relationship. - Negative values indicate a negative relationship. - Zero indicates no linear relationship. **7. Define hypothesis testing and explain the steps involved (formulation, selection of significance level, calculation, interpretation).** - **Hypothesis testing:** Statistical method to make inferences about a population based on sample data. - **Steps:** 1. Formulate null and alternative hypotheses. 2. Set a significance level (α). 3. Collect and analyze data using appropriate statistical tests (e.g., t-test, ANOVA). 4. Compare the calculated test statistic with the critical value or p-value. 5. Make a decision to either reject or fail to reject the null hypothesis based on the evidence. **8. Explain the types of errors in hypothesis testing (Type I and Type II) and how they relate to significance level and power.** - **Type I error:** Incorrectly rejecting a true null hypothesis (false positive). Controlled by lowering the significance level (α). - **Type II error:** Incorrectly failing to reject a false null hypothesis (false negative). Controlled by increasing sample size or improving test sensitivity (power). **9. Describe the difference between parametric and non-parametric tests in psychological research. Provide examples.** - **Parametric tests:** Assume data come from a specific distribution (e.g., normal distribution). Examples include t-tests, ANOVA, Pearson correlation. - **Non-parametric tests:** Do not make assumptions about data distribution. Examples include Mann-Whitney U test, Wilcoxon signed-rank test, Spearman correlation. **10. Explain analysis of variance (ANOVA) and its use in psychological research.** - **ANOVA:** Compares means between three or more groups to determine if there are statistically significant differences. - Used in psychology to analyze data from experiments with multiple treatment conditions or independent variables. **11. Define regression analysis and provide examples of its application in psychology.** - **Regression analysis:** Examines the relationship between one or more independent variables (predictors) and a dependent variable (outcome). - Examples include predicting academic performance based on study hours, or predicting happiness based on income and social support. **12. Discuss effect size measures (e.g., Cohen\'s d, eta-squared) and their interpretation in psychological research.** - **Effect size:** Quantifies the strength of a relationship or the magnitude of a difference between variables. - **Examples:** - **Cohen\'s d:** Standardized difference between two means. ** - **Eta-squared (η²):** Proportion of variance in the dependent variable explained by the independent variable(s). ** **13. Explain the concept of statistical power and its importance in psychological research.** - **Statistical power:** Probability that a study will detect an effect when it exists (1 - β). - Important because low power increases the risk of Type II errors (false negatives) and affects the reliability of study results. **14. Define meta-analysis and discuss its role in integrating research findings in psychology.** - **Meta-analysis:** Statistical method for combining results from multiple studies to synthesize findings and draw general conclusions about a research question. - Provides a comprehensive overview of existing literature, quantifies overall effect sizes, and identifies patterns or inconsistencies across studies. **15. Describe ethical considerations in psychological research involving statistical analysis.** - Includes obtaining informed consent, protecting participant confidentiality, minimizing harm and distress, ensuring voluntary participation, and maintaining integrity in data collection, analysis, and reporting. **16. What are the assumptions underlying parametric tests such as t-tests and ANOVA?** - Assumptions include: - Normality: Data should be normally distributed. - Homogeneity of variance: Variances of groups being compared should be approximately equal. - Independence: Observations should be independent of each other. **17. Discuss the role of probability theory in psychological statistics.** - Probability theory provides a framework for quantifying uncertainty and randomness in data. - It is fundamental in hypothesis testing, determining the likelihood of events, and interpreting results from statistical tests. **18. Explain the concept of sampling distribution and its importance in inferential statistics.** - **Sampling distribution:** Distribution of sample statistics (e.g., means, proportions) obtained from multiple samples of the same size taken from a population. - It is important because it allows researchers to make inferences about population parameters based on sample statistics, using techniques like confidence intervals and hypothesis testing. **19. Define standard error and its relationship to sample size and variability.** - **Standard error:** Standard deviation of the sampling distribution of a statistic (e.g., mean). - It decreases with larger sample sizes and lower variability in the data, indicating greater precision in estimating population parameters. **20. What is the Central Limit Theorem (CLT) and why is it important in statistical inference?** - **Central Limit Theorem:** States that with a sufficiently large sample size, the sampling distribution of the sample mean will be approximately normally distributed, regardless of the population distribution. - Important because it justifies the use of parametric tests even when population data are not normally distributed, as long as sample sizes are large enough. **21. Describe the steps involved in conducting a correlation analysis.** - Choose variables: Select variables with a plausible theoretical relationship. - Calculate correlation coefficient: Use a formula like Pearson\'s r to quantify the strength and direction of the relationship. - Interpret results: Determine if the correlation is statistically significant and interpret its meaning in the context of the research question. **22. Discuss the importance of reliability and validity in psychological measurement.** - **Reliability:** Consistency and stability of measurement over time and across different conditions. - **Validity:** The extent to which a test measures what it intends to measure. - Both are crucial for ensuring that research findings are accurate, replicable, and meaningful. **23. Explain the difference between descriptive and inferential statistics. Provide examples of each in psychological research.** - **Descriptive statistics:** Summarize and describe characteristics of a data set (e.g., mean, standard deviation). - **Inferential statistics:** Make inferences and generalizations about populations based on sample data (e.g., t-tests, ANOVA). **24. What are some common biases or errors to watch out for in psychological research?** - **Selection bias:** Non-random selection of participants leading to unrepresentative samples. - **Confirmation bias:** Seeking or interpreting evidence in ways that confirm preconceptions. - **Reporting bias:** Selective reporting of results that support a hypothesis while ignoring contradictory findings. **25. Define factorial design in psychological research and discuss its advantages.** - **Factorial design:** Experimental design involving the simultaneous manipulation of two or more independent variables (factors). - Advantages include the ability to examine main effects of each factor, interactions between factors, and efficiency in studying multiple variables simultaneously. **26. Explain interaction effects in factorial designs. Provide an example relevant to psychology.** - **Interaction effects:** When the effect of one independent variable on the dependent variable differs depending on the level of another independent variable. - Example: Interaction between personality type (introvert vs. extrovert) and teaching style (interactive vs. lecture-based) on learning outcomes in students. **27. Discuss the difference between within-subjects and between-subjects designs.** - **Within-subjects design:** Participants experience all conditions of the experiment, serving as their own control. - **Between-subjects design:** Different groups of participants are exposed to different conditions or levels of the independent variable. - Each design has strengths and weaknesses in controlling for confounding variables and understanding causal relationships. **28. Define Bayesian statistics and contrast it with frequentist statistics.** - **Bayesian statistics:** Incorporates prior beliefs or knowledge (prior probabilities) and updates them with new evidence to calculate posterior probabilities. Emphasizes uncertainty and subjective interpretations. - **Frequentist statistics:** Focuses on probabilities derived from sample data alone, without incorporating prior beliefs. Uses p-values and confidence intervals for inference. **29. Describe the role of effect size measures in psychological research.** - Effect size measures quantify the magnitude of a relationship or the strength of an effect independent of sample size. - They help researchers evaluate the practical significance of findings, compare results across studies, and interpret the real-world implications of their research. **30. Explain the purpose and interpretation of meta-analysis in psychology.** - **Meta-analysis:** Statistical technique for combining results from multiple studies to obtain an overall estimate of the effect size. - It provides a comprehensive summary of existing research, identifies patterns or inconsistencies across studies, and enhances the reliability and generalizability of findings. **31. Discuss the ethical considerations in psychological research involving statistical analysis.** - Ethical considerations include obtaining informed consent, protecting participant confidentiality, minimizing harm and distress, ensuring voluntary participation, and maintaining integrity in data collection, analysis, and reporting. **32. Define statistical power and its importance in psychological research.** - **Statistical power:** Probability that a study will detect an effect when it exists (1 - β). - Important because low power increases the risk of Type II errors (false negatives) and affects the reliability of study results. **33. Explain the concept of a p-value in hypothesis testing.** - **P-value:** Probability of obtaining results as extreme as observed, assuming the null hypothesis is true. - It indicates the strength of evidence against the null hypothesis: a lower p-value suggests stronger evidence against the null hypothesis and supports the rejection of the null hypothesis in favor of the alternative hypothesis. **34. What are the steps involved in conducting a t-test? Explain with an example.** - **Steps:** 1. State the null (H₀) and alternative (H₁) hypotheses. 2. Choose an appropriate type of t-test (e.g., independent samples t-test, paired samples t-test). 3. Calculate the t-statistic using the formula: 4. Determine degrees of freedom (df) and find the critical value or p-value. 5. Compare the calculated t-value with the critical value or p-value to make a decision regarding the null hypothesis. - Example: Compare the mean scores of two groups (Group A and Group B) on a memory test to determine if there is a significant difference in memory performance between the groups. **35. Discuss the importance of random assignment in experimental design.** - **Random assignment:** Assigning participants to experimental conditions or groups randomly to minimize the influence of confounding variables and ensure that each participant has an equal chance of being assigned to any condition. - It enhances internal validity by reducing bias and allowing researchers to infer cause-and-effect relationships between variables. **36. Define multivariate analysis and its application in psychological research.** - **Multivariate analysis:** Analyzes multiple dependent variables simultaneously to understand complex relationships among variables. - Applications include identifying patterns of behavior or personality traits, examining interactions between variables, and predicting outcomes based on multiple predictors. **37. Explain the concepts of type I and type II errors in hypothesis testing.** - **Type I error:** Incorrectly rejecting a true null hypothesis (false positive). - **Type II error:** Incorrectly failing to reject a false null hypothesis (false negative). - Balancing these errors involves choosing an appropriate significance level (α) and considering statistical power to minimize both types of errors. **38. Describe the assumptions underlying the use of analysis of variance (ANOVA) in psychological research.** - Assumptions include: - Normality: Residuals (errors) are normally distributed. - Homogeneity of variances: Variances of groups being compared are approximately equal. - Independence: Observations within each group are independent of each other. **39. Discuss the advantages and disadvantages of using non-parametric tests in psychological research.** - **Advantages:** Robust to violations of assumptions (e.g., normality, homogeneity of variance), applicable to ordinal or non-normally distributed data, and easier to interpret without stringent requirements. - **Disadvantages:** Less powerful than parametric tests with normally distributed data, may lose efficiency with larger sample sizes, and provide less precise estimates of population parameters. **40. Explain the purpose of a chi-square test and provide an example of its application in psychological research.** - **Chi-square test:** Determines if there is a significant association between categorical variables in a contingency table. - Example: Assessing whether there is a relationship between gender (male vs. female) and voting preference (candidate A vs. candidate B) in a political survey. **41. Define factor analysis and its role in psychological measurement.** - **Factor analysis:** Statistical technique that identifies patterns or underlying dimensions (factors) among a set of variables. - It reduces data complexity by grouping variables based on shared variance, aiding in the development of more concise and interpretable measurement scales in psychology. **42. Discuss the concept of reliability in psychological measurement. What are the different types of reliability?** - **Reliability:** Consistency and stability of measurement over time and across different conditions. - **Types:** - **Test-retest reliability:** Consistency of scores across repeated administrations of the same test. - **Internal consistency reliability:** Consistency among items within a measurement scale (e.g., Cronbach\'s alpha). - **Inter-rater reliability:** Consistency between different raters or observers scoring the same behavior or event. **43. Explain the concept of effect size in statistical analysis. Why is it important in interpreting research findings?** - **Effect size:** Quantifies the strength of a relationship or the magnitude of an effect independent of sample size. - Important because it helps researchers assess the practical significance of findings, compare results across studies, and make informed decisions about the relevance of their research in practical settings. **44. Describe the difference between cross-sectional and longitudinal research designs in psychology.** - **Cross-sectional design:** Collects data from participants at a single point in time to compare different groups or assess relationships between variables. - **Longitudinal design:** Follows the same group of participants over an extended period to study developmental changes, stability of behaviors, or the effects of interventions over time. **45. Define the concept of statistical inference and its application in psychological research.** - **Statistical inference:** Using sample data to make generalizations or predictions about a population. - Applications include hypothesis testing, estimating population parameters, and drawing conclusions based on statistical evidence derived from sample data. **46. Explain the purpose of a scatterplot in data visualization and interpretation.** - **Scatterplot:** Graphical representation of the relationship between two continuous variables. - It helps visualize patterns, trends, or relationships in data, assess the strength and direction of correlations, and identify outliers or clusters within the data set. **47. Discuss the role of ethics in statistical analysis and reporting of psychological research.** - Ethics ensures that research is conducted responsibly, with respect for the rights and well-being of participants. - It includes obtaining informed consent, protecting participant confidentiality, minimizing harm and distress, and ensuring honesty and transparency in data collection, analysis, and reporting. **48. Define Bayesian statistics and contrast it with traditional frequentist statistics.** - **Bayesian statistics:** Incorporates prior beliefs or knowledge (prior probabilities) and updates them with new evidence to calculate posterior probabilities. It emphasizes uncertainty and subjective interpretations. - **Frequentist statistics:** Focuses on probabilities derived from sample data alone, without incorporating prior beliefs. It uses p-values and confidence intervals for inference based on observed data. **49. Explain the concept of statistical power and its importance in psychological research.** - **Statistical power:** Probability that a study will detect an effect when it exists (1 - β). - Important because low power increases the risk of Type II errors (false negatives) and affects the reliability and validity of study results. **50. Describe the purpose and interpretation of meta-analysis in psychology.** - **Meta-analysis:** Statistical technique for combining results from multiple studies to obtain an overall estimate of the effect size. - It provides a comprehensive summary of existing research, identifies patterns or inconsistencies across studies, and enhances the reliability and generalizability of findings. **51. Define and differentiate between parametric and non-parametric tests. Give examples of each in psychological research.** - **Parametric tests:** Assume data follow a specific distribution (e.g., normal distribution) and make inferences about population parameters. Examples include t-tests, ANOVA, Pearson correlation. - **Non-parametric tests:** Do not make assumptions about data distribution and are used for ordinal or non-normally distributed data. Examples include Mann-Whitney U test, Wilcoxon signed-rank test, Spearman correlation. **52. Discuss the concept of sampling distribution and its role in statistical inference.** - **Sampling distribution:** Distribution of sample statistics (e.g., means, proportions) obtained from multiple samples of the same size taken from a population. - It serves as the basis for making inferences about population parameters, such as constructing confidence intervals and conducting hypothesis tests. **53. Explain the steps involved in conducting a hypothesis test using the five-step approach.** - **Steps:** 1. State the null (H₀) and alternative (H₁) hypotheses. 2. Set the significance level (α). 3. Collect data and choose an appropriate statistical test. 4. Calculate the test statistic and determine the p-value. 5. Make a decision to either reject or fail to reject the null hypothesis based on the p-value and compare it with α. **54. Define the concept of effect size in statistical analysis. How is it interpreted in psychological research?** - **Effect size:** Quantitative measure of the magnitude of a phenomenon or the strength of a relationship between variables. - In psychological research, effect sizes help interpret the practical significance of findings, compare results across studies, and assess the impact of interventions or treatments. **55. Describe the purpose and interpretation of confidence intervals in psychological research.** - **Confidence interval:** Range of values calculated from sample data that is likely to include the true population parameter, with a specified level of confidence (e.g., 95% confidence). - It provides a measure of the precision and uncertainty associated with sample estimates, helping researchers make inferences about population parameters. **56. Discuss the assumptions and applications of regression analysis in psychological research.** - **Assumptions:** Linearity (relationship between variables), independence of errors, homoscedasticity (constant variance of errors), and normality of residuals. - **Applications:** Predicting outcomes based on predictor variables, identifying significant predictors of behavior or outcomes, and understanding relationships between variables. **57. Explain the concept of reliability in psychological measurement. What are the different types of reliability?** - **Reliability:** Consistency and stability of measurement over time and across different conditions. - **Types:** - **Test-retest reliability:** Consistency of scores across repeated administrations of the same test. - **Internal consistency reliability:** Consistency among items within a measurement scale (e.g., Cronbach\'s alpha). - **Inter-rater reliability:** Consistency between different raters or observers scoring the same behavior or event. **58. Define factorial design in psychological research. How is it used to study interactions between variables?** - **Factorial design:** Experimental design involving the simultaneous manipulation of two or more independent variables (factors). - It allows researchers to study main effects of each factor and interactions between factors, providing insights into how different variables interact to influence outcomes. **59. Discuss the concept of statistical power and its importance in psychological research.** - **Statistical power:** Probability that a study will detect an effect when it exists (1 - β). - Important because low power increases the risk of Type II errors (false negatives) and affects the reliability and validity of study results. **60. Describe the purpose and interpretation of meta-analysis in psychology.** - **Meta-analysis:** Statistical technique for combining results from multiple studies to obtain an overall estimate of the effect size. - It provides a comprehensive summary of existing research, identifies patterns or inconsistencies across studies, and enhances the reliability and generalizability of findings. **61. Explain the difference between reliability and validity in psychological measurement. Why are both important?** - **Reliability:** Consistency and stability of measurement over time and across different conditions. - **Validity:** The extent to which a test measures what it intends to measure. - Both are important because reliable measures are consistent and stable, while valid measures accurately assess the construct of interest, ensuring meaningful and trustworthy results in psychological research. **62. Define the concept of statistical inference and its application in psychological research.** - **Statistical inference:** Using sample data to make generalizations or predictions about a population. - Applications include hypothesis testing, estimating population parameters, and drawing conclusions based on statistical evidence derived from sample data. **63. Explain the purpose of a scatterplot in data visualization and interpretation.** - **Scatterplot:** Graphical representation of the relationship between two continuous variables. - It helps visualize patterns, trends, or relationships in data, assess the strength and direction of correlations, and identify outliers or clusters within the data set. **64. Discuss the role of ethics in statistical analysis and reporting of psychological research.** - Ethics ensures that research is conducted responsibly, with respect for the rights and well-being of participants. - It includes obtaining informed consent, protecting participant confidentiality, minimizing harm and distress, and ensuring honesty and transparency in data collection, analysis, and reporting. **65. Define Bayesian statistics and contrast it with traditional frequentist statistics.** - **Bayesian statistics:** Incorporates prior beliefs or knowledge (prior probabilities) and updates them with new evidence to calculate posterior probabilities. It emphasizes uncertainty and subjective interpretations. - **Frequentist statistics:** Focuses on probabilities derived from sample data alone, without incorporating prior beliefs. It uses p-values and confidence intervals for inference based on observed data. **66. Explain the concept of statistical power and its importance in psychological research.** - **Statistical power:** Probability that a study will detect an effect when it exists (1 - β). - Important because low power increases the risk of Type II errors (false negatives) and affects the reliability and validity of study results. **67. Describe the purpose and interpretation of meta-analysis in psychology.** - **Meta-analysis:** Statistical technique for combining results from multiple studies to obtain an overall estimate of the effect size. - It provides a comprehensive summary of existing research, identifies patterns or inconsistencies across studies, and enhances the reliability and generalizability of findings. **68. Define and differentiate between parametric and non-parametric tests. Give examples of each in psychological research.** - **Parametric tests:** Assume data follow a specific distribution (e.g., normal distribution) and make inferences about population parameters. Examples include t-tests, ANOVA, Pearson correlation. - **Non-parametric tests:** Do not make assumptions about data distribution and are used for ordinal or non-normally distributed data. Examples include Mann-Whitney U test, Wilcoxon signed-rank test, Spearman correlation. **69. Discuss the concept of sampling distribution and its role in statistical inference.** - **Sampling distribution:** Distribution of sample statistics (e.g., means, proportions) obtained from multiple samples of the same size taken from a population. - It serves as the basis for making inferences about population parameters, such as constructing confidence intervals and conducting hypothesis tests. **70. Explain the steps involved in conducting a hypothesis test using the five-step approach.** - **Steps:** 1. State the null (H₀) and alternative (H₁) hypotheses. 2. Set the significance level (α). 3. Collect data and choose an appropriate statistical test. 4. Calculate the test statistic and determine the p-value. 5. Make a decision to either reject or fail to reject the null hypothesis based on the p-value and compare it with α. **71. Define the concept of effect size in statistical analysis. How is it interpreted in psychological research?** - **Effect size:** Quantitative measure of the magnitude of a phenomenon or the strength of a relationship between variables. - In psychological research, effect sizes help interpret the practical significance of findings, compare results across studies, and assess the impact of interventions or treatments. **72. Describe the purpose and interpretation of confidence intervals in psychological research.** - **Confidence interval:** Range of values calculated from sample data that is likely to include the true population parameter, with a specified level of confidence (e.g., 95% confidence). - It provides a measure of the precision and uncertainty associated with sample estimates, helping researchers make inferences about population parameters. **73. Discuss the assumptions and applications of regression analysis in psychological research.** - **Assumptions:** Linearity (relationship between variables), independence of errors, homoscedasticity (constant variance of errors), and normality of residuals. - **Applications:** Predicting outcomes based on predictor variables, identifying significant predictors of behavior or outcomes, and understanding relationships between variables. **74. Explain the concept of reliability in psychological measurement. What are the different types of reliability?** - **Reliability:** Consistency and stability of measurement over time and across different conditions. - **Types:** - **Test-retest reliability:** Consistency of scores across repeated administrations of the same test. - **Internal consistency reliability:** Consistency among items within a measurement scale (e.g., Cronbach\'s alpha). - **Inter-rater reliability:** Consistency between different raters or observers scoring the same behavior or event. **75. Define factorial design in psychological research. How is it used to study interactions between variables?** - **Factorial design:** In psychological research, factorial design involves manipulating two or more independent variables (factors) simultaneously to study their effects on a dependent variable. This design allows researchers to examine both main effects (individual effects of each independent variable) and interaction effects (combined effects of multiple variables that are not simply additive). - **Example:** Suppose a researcher wants to study the effects of both gender and type of therapy on depression levels. They might design a 2x2 factorial study where: - Factor 1: Gender (Male vs. Female) - Factor 2: Type of Therapy (Cognitive Behavioral Therapy vs. Pharmacological Therapy) - **Purpose:** Factorial designs are used to: - Explore how different variables interact to influence outcomes. - Determine if the effect of one variable depends on the level of another variable (interaction effect). - Provide a more comprehensive understanding of the relationships between variables compared to single-factor studies. **76. Discuss the concept of statistical power and its importance in psychological research.** - **Statistical power:** Probability that a study will detect an effect when it exists (1 - β). - Important because low power increases the risk of Type II errors (false negatives) and affects the reliability and validity of study results. **77. Describe the purpose and interpretation of meta-analysis in psychology.** - **Meta-analysis:** Statistical technique for combining results from multiple studies to obtain an overall estimate of the effect size. - It provides a comprehensive summary of existing research, identifies patterns or inconsistencies across studies, and enhances the reliability and generalizability of findings. **78. Define and differentiate between parametric and non-parametric tests. Give examples of each in psychological research.** - **Parametric tests:** Assume data follow a specific distribution (e.g., normal distribution) and make inferences about population parameters. Examples include t-tests, ANOVA, Pearson correlation. - **Non-parametric tests:** Do not make assumptions about data distribution and are used for ordinal or non-normally distributed data. Examples include Mann-Whitney U test, Wilcoxon signed-rank test, Spearman correlation. **79. Discuss the concept of sampling distribution and its role in statistical inference.** - **Sampling distribution:** Distribution of sample statistics (e.g., means, proportions) obtained from multiple samples of the same size taken from a population. - It serves as the basis for making inferences about population parameters, such as constructing confidence intervals and conducting hypothesis tests. **80. Explain the steps involved in conducting a hypothesis test using the five-step approach.** - **Steps:** 1. State the null (H₀) and alternative (H₁) hypotheses. 2. Set the significance level (α). 3. Collect data and choose an appropriate statistical test. 4. Calculate the test statistic and determine the p-value. 5. Make a decision to either reject or fail to reject the null hypothesis based on the p-value and compare it with α. **81. Define the concept of effect size in statistical analysis. How is it interpreted in psychological research?** - **Effect size:** Quantitative measure of the magnitude of a phenomenon or the strength of a relationship between variables. - In psychological research, effect sizes help interpret the practical significance of findings, compare results across studies, and assess the impact of interventions or treatments. **82. Describe the purpose and interpretation of confidence intervals in psychological research.** - **Confidence interval:** Range of values calculated from sample data that is likely to include the true population parameter, with a specified level of confidence (e.g., 95% confidence). - It provides a measure of the precision and uncertainty associated with sample estimates, helping researchers make inferences about population parameters. **83. Discuss the assumptions and applications of regression analysis in psychological research.** - **Assumptions:** Linearity (relationship between variables), independence of errors, homoscedasticity (constant variance of errors), and normality of residuals. - **Applications:** Predicting outcomes based on predictor variables, identifying significant predictors of behavior or outcomes, and understanding relationships between variables. **84. Explain the concept of reliability in psychological measurement. What are the different types of reliability?** - **Reliability:** Consistency and stability of measurement over time and across different conditions. - **Types:** - **Test-retest reliability:** Consistency of scores across repeated administrations of the same test. - **Internal consistency reliability:** Consistency among items within a measurement scale (e.g., Cronbach\'s alpha). - **Inter-rater reliability:** Consistency between different raters or observers scoring the same behavior or event. **85. Define factorial design in psychological research. How is it used to study interactions between variables?** - **Factorial design:** Experimental design involving the simultaneous manipulation of two or more independent variables (factors). - It allows researchers to study main effects of each factor and interactions between factors, providing insights into how different variables interact to influence outcomes. **86. Discuss the concept of statistical power and its importance in psychological research.** - **Statistical power:** Probability that a study will detect an effect when it exists (1 - β). - Important because low power increases the risk of Type II errors (false negatives) and affects the reliability and validity of study results. **87. Describe the purpose and interpretation of meta-analysis in psychology.** - **Meta-analysis:** Statistical technique for combining results from multiple studies to obtain an overall estimate of the effect size. - It provides a comprehensive summary of existing research, identifies patterns or inconsistencies across studies, and enhances the reliability and generalizability of findings. **88. Define and differentiate between parametric and non-parametric tests. Give examples of each in psychological research.** - **Parametric tests:** Assume data follow a specific distribution (e.g., normal distribution) and make inferences about population parameters. Examples include t-tests, ANOVA, Pearson correlation. - **Non-parametric tests:** Do not make assumptions about data distribution and are used for ordinal or non-normally distributed data. Examples include Mann-Whitney U test, Wilcoxon signed-rank test, Spearman correlation. **89. Discuss the concept of sampling distribution and its role in statistical inference.** - **Sampling distribution:** Distribution of sample statistics (e.g., means, proportions) obtained from multiple samples of the same size taken from a population. - It serves as the basis for making inferences about population parameters, such as constructing confidence intervals and conducting hypothesis tests. **90. Explain the steps involved in conducting a hypothesis test using the five-step approach.** - **Steps:** 1. State the null (H₀) and alternative (H₁) hypotheses. 2. Set the significance level (α). 3. Collect data and choose an appropriate statistical test. 4. Calculate the test statistic and determine the p-value. 5. Make a decision to either reject or fail to reject the null hypothesis based on the p-value and compare it with α. **91. Define the concept of effect size in statistical analysis. How is it interpreted in psychological research?** - **Effect size:** Quantitative measure of the magnitude of a phenomenon or the strength of a relationship between variables. - In psychological research, effect sizes help interpret the practical significance of findings, compare results across studies, and assess the impact of interventions or treatments. **92. Describe the purpose and interpretation of confidence intervals in psychological research.** - **Confidence interval:** Range of values calculated from sample data that is likely to include the true population parameter, with a specified level of confidence (e.g., 95% confidence). - It provides a measure of the precision and uncertainty associated with sample estimates, helping researchers make inferences about population parameters. **93. Discuss the assumptions and applications of regression analysis in psychological research.** - **Assumptions:** Linearity (relationship between variables), independence of errors, homoscedasticity (constant variance of errors), and normality of residuals. - **Applications:** Predicting outcomes based on predictor variables, identifying significant predictors of behavior or outcomes, and understanding relationships between variables. **94. Explain the concept of reliability in psychological measurement. What are the different types of reliability?** - **Reliability:** Consistency and stability of measurement over time and across different conditions. - **Types:** - **Test-retest reliability:** Consistency of scores across repeated administrations of the same test. - **Internal consistency reliability:** Consistency among items within a measurement scale (e.g., Cronbach\'s alpha). - **Inter-rater reliability:** Consistency between different raters or observers scoring the same behavior or event. **95. Define factorial design in psychological research. How is it used to study interactions between variables?** - **Factorial design:** Experimental design involving the simultaneous manipulation of two or more independent variables (factors). - It allows researchers to study main effects of each factor and interactions between factors, providing insights into how different variables interact to influence outcomes. **96. Discuss the concept of statistical power and its importance in psychological research.** - **Statistical power:** Probability that a study will detect an effect when it exists (1 - β). - Important because low power increases the risk of Type II errors (false negatives) and affects the reliability and validity of study results. **97. Describe the purpose and interpretation of meta-analysis in psychology.** - **Meta-analysis:** Statistical technique for combining results from multiple studies to obtain an overall estimate of the effect size. - It provides a comprehensive summary of existing research, identifies patterns or inconsistencies across studies, and enhances the reliability and generalizability of findings. **98. Define and differentiate between parametric and non-parametric tests. Give examples of each in psychological research.** - **Parametric tests:** Assume data follow a specific distribution (e.g., normal distribution) and make inferences about population parameters. Examples include t-tests, ANOVA, Pearson correlation. - **Non-parametric tests:** Do not make assumptions about data distribution and are used for ordinal or non-normally distributed data. Examples include Mann-Whitney U test, Wilcoxon signed-rank test, Spearman correlation. **99. Discuss the concept of sampling distribution and its role in statistical inference.** - **Sampling distribution:** Distribution of sample statistics (e.g., means, proportions) obtained from multiple samples of the same size taken from a population. - It serves as the basis for making inferences about population parameters, such as constructing confidence intervals and conducting hypothesis tests. 100\. Explain the steps involved in conducting a hypothesis test using the five-step approach. - **Steps:** 1. **State the null (H₀) and alternative (H₁) hypotheses:** Define the hypotheses that represent the researcher\'s expectations or claims about the population parameters. 2. **Set the significance level (α):** Determine the acceptable level of Type I error (probability of rejecting the null hypothesis when it is actually true), typically set at 0.05 or 0.01. 3. **Collect data and choose an appropriate statistical test:** Gather data from a sample and select a statistical test based on the research question and nature of data (e.g., t-test, ANOVA, chi-square test). 4. **Calculate the test statistic and determine the p-value:** Compute the test statistic (e.g., t-value, F-value, chi-square statistic) using sample data and find the corresponding p-value. 5. **Make a decision:** Compare the p-value with the significance level (α). If the p-value is less than α, reject the null hypothesis; if the p-value is greater than or equal to α, fail to reject the null hypothesis. Ps. Sana tama to AHAHAAHA Good luck Ara ❤️