Standard error-Biostatistics
- 1. K.SUDHA RAMESHWARI ASSISTANT PROFESSOR, V.V.VANNIAPERUMAL COLLEGE FOR WOMEN,VIRUDHUNAGAR TAMILNADU,INDIA
- 2. The standard deviation of the sampling distribution is called standard error The following is the list of formula for obtaining standard error for different statistics
- 6. Utility of standard error It is used as an instrument in testing a given hypothesis. When the hypothesis is tested at 5% level of significance , if the difference between observed and expected mean is more than 1.96 standard error, we may conclude that the result of the experiment does not support the hypothesis at 5% level and if the difference is less than 1.96 SE, we can accept the hypothesis. At 1% significance level, if the difference is more than 2.58 SE, we can’t accept the hypothesisa and vice versa
- 7. Tests of significance can be classified into three 1. Tests of significance for attributes 2. Tests if significance for variables (large samples) 3. Tests of significance for variables (small samples)
- 8. Application of tests of significance in biological sciences Tests of significance of attributes: 1. To find out whether both vegetarian and non- vegetarian food eaters are equally intelligent or not 2. To test the hypothesis that male and female babies are born in equal number When we take two samples from two different populations, we can find out whether there is any significant difference in the proportion of success. Examples -To find out whether there is any significant difference in the food habits of two villagers
- 9. Tests of significance of large samples: A sample is considered to be large when its size exceeds 30 (n>30) The various fields of applications are: 1. To test the hypothesis that there is no significant difference between sample mean height and hypothetical mean height of sugarcane 2. To test the hypothesis that there is no significant difference in the yield of grains between the two varieties
- 10. 3.To test the hypothesis that there is no significant difference between mean accidents in the two towns 4. To test the hypothesis that there is no significant difference in standard deviation of yield between paddy and wheat 5. To test the hypothesis that Indian paddy plants are on an average not taller than Australian paddy plants
- 11. Tests of significance of small samples Small samples means (n<30) t-test and F-test are applied Applications of t-test are 1. To find out whether systolic blood pressure of a sample of 10 persons in the age group of 40-50 is significantly differ from the hypothetical value of blood pressure of the population 2. To test the hypothesis that the average weight of goat population is 12kgs.
- 12. Reference Statistical methods for biologists(Biostatistics)- S.Palanichamy, M.Manoharan