Basic Statistics PDF

A Quick Guide on BASIC STATISTICS STATISTICS is a mathematical science including methods of collecting, organizing and analyzing data in such a way that meaningful conclusions can be drawn from them.  TWO CATEGORIES OF STATISTICS DESCRIPTIVE STATISTICS used to describe a mass of data i...

A Quick Guide on BASIC STATISTICS STATISTICS is a mathematical science including methods of collecting, organizing and analyzing data in such a way that meaningful conclusions can be drawn from them.  TWO CATEGORIES OF STATISTICS DESCRIPTIVE STATISTICS used to describe a mass of data in a clear, concise, and informative way  TWO CATEGORIES OF STATISTICS INFERENTIAL STATISTICS concerned with making generalizations about the characteristics of a larger set where only a part is examined  TWO CATEGORIES OF STATISTICS DESCRIPTIVE INFERENTIAL Mean Median Parametric Tests like: Mode T-test ANOVA Range Correlation Standard Deviation Variance REMINDERS: There are data assumptions required when using these parametric tests (e.g. normality of data, no outliers). DEPENDENT SAMPLE T-TEST used to compare two Reject NULL different groups of data from HYPOTHESIS if p- one population value ("Sig.") < significance level (α) it answers if there is a significant effect between those two groups/ population.  Example: Effect of Sugarcane Extract to the Blood Glucose Level of White Mice What we know: We have two groups of data: BEFORE & AFTER/ PRETEST & POST-TEST/ INITIAL & FINAL Data we have: blood glucose level of white mice before and after treatment INDEPENDENT SAMPLE T-TEST used to compare data from Reject NULL two groups HYPOTHESIS if p- value ("Sig.") < it answers if there is a significance level (α) significant difference between those two groups/ population.  Example: Perception of Grade 12 Students and teachers of TNHS regarding the Philippine Government Response to COVID-19 What we know: We have two groups: Grade 12 students and Grade 12 teachers Data we have: mean score of their perception on government response  Analysis of Variance used to compare data from Reject NULL three or more groups HYPOTHESIS if p- value ("Sig.") < it answers if there is a significance level (α) significant difference between those groups/ population.  Example: eHealth Literacy and Fear of COVID-19: Survey among Grade 12 Academic Track Students of Tarlac National High School What we know: We have three groups to compare: STEM, ABM, HUMSS students Data we have: mean score of their eHealth Literacy  PEARSON CORRELATION used to measure whether there Reject NULL exist a statistically significant HYPOTHESIS if p- linear relationship between two value ("Sig.") < continuous variables significance level (α) it also gives us the strength of linear relationship (weak/ strong) and direction of relationship (positive or negative)  Example: The Relationship of SHS Students' English Language Proficiency to their Science Performance What we know: We have continuous variables. (English proficiency test score & average grade in Science SHS Subjects)  CHI-SQUARE TEST used to measure whether there exist a statistically significant association between two categorical variables Reject NULL HYPOTHESIS if p- value ("Sig.") < significance level (α)  Example: The Relationship of Family Structure and Academic Performance of Grade 12 STEM Students What we know: Family Structure: 0- Nuclear Family 1- Single Parent Academic Performance: 1- 85-89, 2- 90-94, 3- 95-99 Questions?