Very good statistics-overview rbc (1)
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This document provides an overview of basic statistics, including descriptive and inferential statistics. Descriptive statistics such as frequencies, percentages, means and standard deviations are used to summarize single variables. Inferential statistics such as correlation, t-tests, chi-square, and logistic regression are used to test hypotheses, determine associations between variables, and make predictions. The document explains when each statistical test is appropriate and how to interpret and report the results.
![How do you report t-tests results?
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Very good statistics-overview rbc (1)
- 1. Basic Statistics AIJAZ SOHAG MSc (Env:Sc),M.A.S(H.S.A.),MBA(Health Mgt),MPH,PhD
- 2. Preface • The purpose of this presentation is to help you determine which statistical tests are appropriate for analyzing your data for research project. • Statistical tests that are presented here focuses on the most common techniques.
- 3. Outline • Descriptive Statistics – Frequencies & percentages – Means & standard deviations • Inferential Statistics – Correlation – T-tests – Chi-square – Logistic Regression
- 4. Types of Statistics/Analyses Descriptive Statistics – Frequencies – Basic measurements Inferential Statistics – Hypothesis Testing – Correlation – Confidence Intervals – Significance Testing – Prediction Describing a phenomena How many? How much? BP, HR, BMI, IQ, etc. Inferences about a phenomena Proving or disproving theories Associations between phenomena If sample relates to the larger population E.g., Diet and health
- 5. Descriptive Statistics Descriptive statistics can be used to summarize and describe a single variable • Frequencies (counts) & Percentages – Use with categorical (nominal) data • Levels, types, groupings, yes/no, Drug A vs. Drug B • Means & Standard Deviations – Use with continuous data • Height, weight, cholesterol, scores on a test
- 6. Frequencies & Percentages Look at the different ways we can display frequencies and percentages for this data: Table Bar chart Pie chart Good if more than 20 observations AKA frequency distributions – good if more than 20 observations
- 7. Continuous Categorical It is possible to take continuous data (such as hemoglobin levels) and turn it into categorical data by grouping values together. Then we can calculate frequencies and percentages for each group.
- 8. Continuous Categorical Distribution of Glasgow Coma Scale Scores Even though this is continuous data, it is being treated as “nominal” as it is broken down into groups or categoriesTip: It is usually better to collect continuous data and then break it down into categories for data analysis as opposed to collecting data that fits into preconceived categories.
- 9. Ordinal Level Data Frequencies and percentages can be computed for ordinal data – Examples: Likert Scales (Strongly Disagree to Strongly Agree); High School/Some College/College Graduate/Graduate School 0 10 20 30 40 50 60 Strongly Agree Agree Disagree Strongly Disagree
- 10. INFERENTIAL STATISTICS Inferential statistics can be used to prove or disprove theories, determine associations between variables, and determine if findings are significant and whether or not we can generalize from our sample to the entire population The types of inferential statistics we will go over: • Correlation • T-tests/ANOVA • Chi-square • Logistic Regression
- 11. Correlation • When to use it? – When you want to know about the association or relationship between two continuous variables • Ex) food intake and weight; drug dosage and blood pressure; air temperature and metabolic rate, etc. • What does it tell you? – If a linear relationship exists between two variables, and how strong that relationship is • What do the results look like? – The correlation coefficient = Pearson’s r – Ranges from -1 to +1 – See next slide for examples of correlation results
- 12. Correlation Guide for interpreting strength of correlations: 0 – 0.25 = Little or no relationship 0.25 – 0.50 = Fair degree of relationship 0.50 - 0.75 = Moderate degree of relationship 0.75 – 1.0 = Strong relationship 1.0 = perfect correlation
- 13. Correlation • How do you interpret it? – If r is positive, high values of one variable are associated with high values of the other variable (both go in SAME direction - ↑↑ OR ↓↓) • Ex) Diastolic blood pressure tends to rise with age, thus the two variables are positively correlated – If r is negative, low values of one variable are associated with high values of the other variable (opposite direction - ↑↓ OR ↓ ↑) • Ex) Heart rate tends to be lower in persons who exercise frequently, the two variables correlate negatively – Correlation of 0 indicates NO linear relationship • How do you report it? – “Diastolic blood pressure was positively correlated with age (r = .75, p < . 05).” Tip: Correlation does NOT equal causation!!! Just because two variables are highly correlated, this does NOT mean that one CAUSES the other!!!
- 14. T-tests • When to use them? – Paired t-tests: When comparing the MEANS of a continuous variable in two non-independent samples (i.e., measurements on the same people before and after a treatment) • Ex) Is diet X effective in lowering serum cholesterol levels in a sample of 12 people? • Ex) Do patients who receive drug X have lower blood pressure after treatment then they did before treatment? – Independent samples t-tests: To compare the MEANS of a continuous variable in TWO independent samples (i.e., two different groups of people) • Ex) Do people with diabetes have the same Systolic Blood Pressure as people without diabetes? • Ex) Do patients who receive a new drug treatment have lower blood pressure than those who receive a placebo? Tip: if you have > 2 different groups, you use ANOVA, which compares the means of 3 or more groups
- 15. T-tests • What does a t-test tell you? – If there is a statistically significant difference between the mean score (or value) of two groups (either the same group of people before and after or two different groups of people) • What do the results look like? – Student’s t • How do you interpret it? – By looking at corresponding p-value • If p < .05, means are significantly different from each other • If p > 0.05, means are not significantly different from each other
- 16. How do you report t-tests results? “As can be seen in Figure 1, specialty candidates had significantly higher scores on questions dealing with treatment than residency candidates (t = [insert t-value from stats output], p < .001). “As can be seen in Figure 1, children’s mean reading performance was significantly higher on the post-tests in all four grades, ( t = [insert from stats output], p < .05)”
- 17. Chi-square • When to use it? – When you want to know if there is an association between two categorical (nominal) variables (i.e., between an exposure and outcome) • Ex) Smoking (yes/no) and lung cancer (yes/no) • Ex) Obesity (yes/no) and diabetes (yes/no) • What does a chi-square test tell you? – If the observed frequencies of occurrence in each group are significantly different from expected frequencies (i.e., a difference of proportions)
- 18. Chi-square • What do the results look like? – Chi-square test statistics = X2 • How do you interpret it? – Usually, the higher the chi-square statistic, the greater likelihood the finding is significant, but you must look at the corresponding p-value to determine significance Tip: Chi square requires that there be 5 or more in each cell of a 2x2 table and 5 or more in 80% of cells in larger tables. No cells can have a zero count.
- 19. How do you report chi-square? “Distribution of obesity by gender showed that 171 (38.9%) and 75 (17%) of women were overweight and obese (Type I &II), respectively. Whilst 118 (37.3%) and 12 (3.8%) of men were overweight and obese (Type I & II), respectively (Table-II). The Chi square test shows that these differences are statistically significant (p<0.001).” “248 (56.4%) of women and 52 (16.6%) of men had abdominal obesity (Fig-2). The Chi square test shows that these differences are statistically significant (p<0.001).”
- 20. Logistic Regression • When to use it? – When you want to measure the strength and direction of the association between two variables, where the dependent or outcome variable is categorical (e.g., yes/no) – When you want to predict the likelihood of an outcome while controlling for confounders • Ex) examine the relationship between health behavior (smoking, exercise, low-fat diet) and arthritis (arthritis vs. no arthritis) • Ex) Predict the probability of stroke in relation to gender while controlling for age or hypertension • What does it tell you? – The odds of an event occurring The probability of the outcome event occurring divided by the probability of it not occurring
- 21. Summary of Statistical Tests Statistic Test Type of Data Needed Test Statistic Example Correlation Two continuous variables Pearson’s r Are blood pressure and weight correlated? T-tests/ANOVA Means from a continuous variable taken from two or more groups Student’s t Do normal weight (group 1) patients have lower blood pressure than obese patients (group 2)? Chi-square Two categorical variables Chi-square X2 Are obese individuals (obese vs. not obese) significantly more likely to have a stroke (stroke vs. no stroke)?
- 22. Summary • Descriptive statistics can be used with nominal and ordinal data • Frequencies and percentages describe categorical data and means and standard deviations describe continuous variables • Inferential statistics can be used to determine associations between variables and predict the likelihood of outcomes or events • Inferential statistics tell us if our findings are significant
- 23. Next Steps • Think about the data that you have collected or will collect as part of your research project – What is your research question? – What are you trying to get your data to “say”? – Which statistical tests will best help you answer your research question? – Contact the bio-statistician research coordinator to discuss how to analyze your data!