Choosing Appropriate Statistical Tools in research.pptx

Choosing Appropriate Statistical Tools in research

1. Statistical Tools

2. Introduction Well-designed research requires a well-chosen study sample and a suitable statistical test selection. Improper inferences from it could lead to false conclusions and unethical behavior. Statistical tools are extensively used in academic and research sectors to study human, animal, and material behaviors and reactions. They can be used to evaluate and comprehend any form of data. Some statistical tools can help you see trends, forecast future sales, and create links between causes and effects. 2

3. Mean The arithmetic mean, more commonly known as the average, is the sum of a list of numbers divided by the number of item in the list. The mean is useful in determining the overall trend of a data set providing a rapid snapshot of your data. 9/3/20XX 3

4. Standard Deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation. 4

5. Regression A regression is a statistical technique that relates a dependent variable to one or more independent (explanatory) variables. A regression model is able to show whether changes observed in the dependent variable are associated with changes in one or more of the explanatory variables. It does this by essentially fitting a best-fit line and seeing how the data is dispersed around this line. 5

6. T - test The t -test tells you how significant the differences between group means are. It lets you know if those differences in means could have happened by chance. The t test is usually used when data sets follow a normal distribution, but you don’t know the population variance. The t score is a ratio between the difference between two groups and the difference within the groups. Larger t scores = more difference between groups. Smaller t score = more similarity between groups. 6

7. T - test 7

8. Median Median, in statistics, is the middle value of the given list of data, when arranged in an order. The arrangement of data or observations can be done either in ascending order or descending order. Example: The median of 2,3,4 is 3. In Maths , the median is also a type of average, which is used to find the center value. 8

9. Chi-square A chi-square ( χ2) statistic is a measure of the difference between the observed and expected frequencies of the outcomes of a set of events or variables. It is used for analyzing differences in categorical variables, especially those nominal in nature. Chi-squared, more properly known as Pearson's chi-square test, is a means of statistically evaluating data. It is used when categorical data from a sampling are being compared to expected or "true" results. 9

10. Chi-square 10

11. ANOVA (Analysis of Variance) Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Analysts use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study. The t- and z-test methods developed in the 20th century were used for statistical analysis until 1918, when Ronald Fisher created the analysis of variance method. ANOVA is also called the Fisher analysis of variance, and it is the extension of the t- and z-tests. The term became well-known in 1925, after appearing in Fisher's book, "Statistical Methods for Research Workers.“ It was employed in experimental psychology and later expanded to subjects that were more complex. 11

12. ANOVA (Analysis of Variance) 12

13. Factorial ANOVA (Analysis of Variance) A factorial ANOVA compares means across two or more independent variables. Factorial ANOVA has two or more independent variables that split the sample in four or more groups. The simplest case of a factorial ANOVA uses two binary variables as independent variables, thus creating four groups within the sample. For some statisticians, the factorial ANOVA doesn’t only compare differences but also assumes a cause- effect relationship; this infers that one or more independent, controlled variables (the factors) cause the significant difference of one or more characteristics. The way this works is that the factors sort the data points into one of the groups, causing the difference in the mean value of the groups. 13

14. Factorial ANOVA (Analysis of Variance) 14

15. Correlation Correlation analysis in research is a statistical method used to measure the strength of the linear relationship between two variables and compute their association. Simply put - correlation analysis calculates the level of change in one variable due to the change in the other. Example of correlation analysis Positive correlation: Negative correlation: Weak/Zero correlation: 15

16. Correlation Positive correlation: A positive correlation between two variables means both the variables move in the same direction. An increase in one variable leads to an increase in the other variable and vice versa. For example, spending more time on a treadmill burns more calories. 16

17. Correlation 17

18. Correlation Negative correlation: A negative correlation between two variables means that the variables move in opposite directions. An increase in one variable leads to a decrease in the other variable and vice versa. For example, increasing the speed of a vehicle decreases the time you take to reach your destination. 18

19. Correlation 19

20. Correlation Weak/Zero correlation: No correlation exists when one variable does not affect the other. For example, there is no correlation between the number of years of school a person has attended and the letters in his/her name. 20

21. Correlation 21

22. References 9/3/20XX Presentation Title 22 https://statisticsbyjim.com/basics/mean_average/ https://www.investopedia.com/terms/s/standarddeviation.asp https://www.investopedia.com/terms/r/regression.asp https://byjus.com/maths/median/ https://www.statisticshowto.com/probability-and-statistics/t-test/ https://www.scribbr.com/statistics/chi-square-tests/ https://www.investopedia.com/terms/a/anova.asp https://www.slideshare.net/murathotirajendranathbabu/statistical-tools-in-research-231169355?from_search=1 https://www.statisticssolutions.com/free-resources/directory-of-statistical-analyses/factorial-anova/#:~:text=A%20factorial%20ANOVA%20compares%20means,in%20four%20or%20more%20groups. https://www.questionpro.com/features/correlation-analysis.html#:~:text=What%20is%20correlation%20analysis%3F,the%20change%20in%20the%20other.

23. Thank you 23