Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our User Agreement and Privacy Policy.

Slideshare uses cookies to improve functionality and performance, and to provide you with relevant advertising. If you continue browsing the site, you agree to the use of cookies on this website. See our Privacy Policy and User Agreement for details.

Successfully reported this slideshow.

Like this presentation? Why not share!

- Inferential statistics (2) by rajnulada 10447 views
- Understanding inferential statistics by Hanimarcelo slide... 5385 views
- INFERENTIAL STATISTICS: AN INTRODUC... by John Labrador 4754 views
- Inferential Statistics by ewhite00 6084 views
- 1.1-1.2 Descriptive and Inferential... by mlong24 2835 views
- Saturn brochure by mlong24 5096 views

1,598 views

Published on

Inferential statictis ready go

Published in:
Healthcare

No Downloads

Total views

1,598

On SlideShare

0

From Embeds

0

Number of Embeds

2

Shares

0

Downloads

127

Comments

5

Likes

6

No notes for slide

- 1. Inferential Statistics Dr.Thet Aung Moe( Roll NO- 29 ) Dr. Win Thandar Phyu ( Roll No-27) (1/2015) Diploma In Hospital Administration
- 2. Statistics A field of study concerned with (1) the collection, organization, summarization & analysis of data ; & (2) drawing of inferences about a body of data when only a part of the data is observed . The science of collecting , organizing, presenting , analyzing & interpreting data to assist in making more effective decisions.
- 3. Descriptive Statistics Method of organizing, summarizing & presenting data in an informative way. (eg.Mean BP of patients) Inferential Statistics The method used to find out something about a population , based on a sample .( eg.Mean BP of all adults in a city ) Biostatistics Statistics concerned with biological sciences & medicine. Statistics Descriptive Statistics Inferential Statistics Biostatistics
- 4. Statistics Descriptive Statistics Presenting, organization & summarizing data Inferential Statistics Drawing conclusions about population based on data observed in a sample
- 5. Statistics Inferential Statistics Descriptive Statistics 1. Collecting 2. Organizing 3. Summarizing 4. Presenting Data 1. Making Inference 2. Hypothesis Testing 3. Determining relationships 4. Making Predictions
- 6. Statistical Inference Definition Statistical inference is the procedure by which we reach a conclusion about a population on the basic of the information contained in a sample that has been drawn from that population.
- 7. Inferential Statistics Definition Consist of generalizing from samples to population, performing estimation & hypothesis test ,determining relationship among variables, & making predictions. Descriptions •To describe the chance of an event occurring •Final result will be in the form of probability Examples Predicting effectiveness of a drug , predicting the relationship between death & smoking habit
- 8. Inferential Statistics Inferential Statistics : Inferring population parameters based on samples of data. Parameters: a measurable characteristic of a population Examples :Mean, Median,Proportion, Variance Population Sample 3 Sample 2 Sample 1 Measurement & Data Types are important in Inferential Statistics
- 9. Population Largest collection of entities or values of a random variable for which we have an interest at a particular time. ( eg. weights of infants , weights of adults ) Finite population – fixed numbers of values (eg. Adult population in a village) Infinite population – endless succession of values (eg.OPD patients in a hospital) Sample : A part of population
- 10. Population Sample
- 11. There are many kinds of samples that may be drawn from a population. Not every kind of sample can be used as a basis for making valid inferences about a population. To make a valid inference about a population , we need a scientific sample from the population. The simplest of these scientific samples is the Simple Random Sample.
- 12. Ifwe use the letter N – size of a finite population The letter n - size of a sample , we defined a simple random sample. Definition If a sample of size “n” is drawn from a population of size “N” in such a way that every possible sample of size “n” has the same chance of being selected , the sample is called a simple random sample.
- 13. Random Number Sample Subject Number Age 137 1 43 114 2 66 115 3 61 183 4 64 185 5 65 028 6 38 085 7 59 181 8 57 018 9 57 164 10 50 Sample of 10 Ages drawn from the ages
- 14. The mechanics of drawing a simple random sample is called simple random sampling. May be with replacement or without replacement . As a rule in practice sampling is always done without replacement . Eg. A simple random of size n=10 from a population of size N = 189*
- 15. Random sampling is important role in designing research studies & experiments. Definition A research study is a scientific study of phenomenon of interest . Research studies involve sampling protocols , collecting & analyzing data, & providing valid conclusions based on the results of the analyses. Definition Experiments are a special type of research study in which observations are made after specific manipulations of conditions have been carried out ; they provide the foundation for scientific research.
- 16. Systematic Sampling Definition A sampling method that is widely used in healthcare research is the systemic sample . Medical records which contain raw data used in healthcare research are stored in a file system or a computer. Are easy to select in a systematic way.
- 17. Stratified Random Sampling Definition A method of sampling that involves the divisions of a population into smaller groups known as strata . In stratified random sampling, the strata are formed based on member’s shared attributes or characteristics. A random sample from each stratum is taken in a number proportion to the stratum ‘s size when compared to the population. These subsets of the strata are pooled to form a random sample.
- 18. Two Main Methods Used In Inferential Statistics 1. Estimation: A sample is used to estimate a parameter & confidence interval about the estimate is constructed . 2. Hypothesis : When doing an experiment we assume an hypothesis is called Null Hypothesis. Data are collected & the null hypothesis is compared with them . If the data are very different from the null hypothesis it is rejected. If not, it is accepted.
- 19. Inferential Statistics Estimation Point Interval Hypothesis Testing
- 20. Thank U !

No public clipboards found for this slide

Login to see the comments