Types of samples The best sampling is probability sampling, because it increases the likelihood of obtaining samples that are representative of the population. Probability sampling Representative samples Probability samples are selected in such a way as to be representative of the population. They provide the most valid or credible results because they reflect the characteristics of the population from which they are selected e. There are two types of probability samples:
Banerjee, Department of Community Medicine, D. Patil Medical College, Pune -India. This article has been cited by other articles Probability studies PMC. In fact, the opposite is the case. The present paper Probability studies to put the P value in proper perspective by explaining different types of probabilities, their role in clinical decision making, medical research and hypothesis testing.
Hypothesis testing, P value, Probability The clinician who wishes to remain abreast with the results of medical research needs to develop a statistical sense. For instance, if 25 out of 30 patients were cured with a new drug compared with 15 out of the 30 on placebo, the superiority of the new drug was readily accepted.
In recent years, the presentation of medical research has undergone much transformation. Nowadays, no respectable journal will accept a paper if the results have not been subjected to statistical significance tests.
The use of statistics has accelerated with the ready availability of statistical software. It has now become fashionable to organize workshops on research methodology and biostatistics.
No doubt, this development was long overdue and one concedes that the methodologies of most medical papers have considerably improved in recent years. But at the same time, a new problem has arisen. The reading of medical journals today presupposes considerable statistical knowledge; however, those doctors who are not familiar with statistical theory tend to interpret the results of significance tests uncritically or even incorrectly.
It is often overlooked that the results of a statistical test depend not only on the observed data but also on the choice of statistical model. The statistician doing analysis of the data has a choice between several tests which are based on different models and assumptions.
Unfortunately, many research workers who know little about statistics leave the statistical analysis to statisticians who know little about medicine; and the end result may well be a series of meaningless calculations.
Nothing can be further from the truth. The present paper endeavors to explain the meaning of probability, its role in everyday clinical practice and the concepts behind hypothesis testing. Probability is a recurring theme in medical practice. No doctor who returns home from a busy day at the hospital is spared the nagging feeling that some of his diagnoses may turn out to be wrong, or some of his treatments may not lead to the expected cure.
Encountering the unexpected is an occupational hazard in clinical practice. Doctors after some experience in their profession reconcile to the fact that diagnosis and prognosis always have varying degrees of uncertainty and at best can be stated as probable in a particular case.
Critical appraisal of medical journals also leads to the same gut feeling. One is bombarded with new research results, but experience dictates that well-established facts of today may be refuted in some other scientific publication in the following weeks or months.
When a practicing clinician reads that some new treatment is superior to the conventional one, he will assess the evidence critically, and at best he will conclude that probably it is true. Two types of probabilities The statistical probability concept is so widely prevalent that almost everyone believes that probability is a frequency.
It is not, of course, an ordinary frequency which can be estimated by simple observations, but it is the ideal or truth in the universe, which is reflected by the observed frequency. For example, when we want to determine the probability of obtaining an ace from a pack of cards which, let us assume has been tampered with by a dishonest gamblerwe proceed by drawing a card from the pack a large number of times, as we know in the long run, the observed frequency will approach the true probability or truth in the universe.
Mathematicians often state that a probability is a long-run frequency, and a probability that is defined in this way is called a frequential probability. The exact magnitude of a frequential probability will remain elusive as we cannot make an infinite number of observations; but when we have made a decent number of observations adequate sample sizewe can calculate the confidence intervals, which are likely to include the true frequential probability.
The width of the confidence interval depends on the number of observations sample size.
The frequential probability concept is so prevalent that we tend to overlook terms like chance, risk and odds, in which the term probability implies a different meaning. Few hypothetical examples will make this clear.
A probabilistic statement incorporates some amount of uncertainty, which may be quantified as follows: A politician may state that there is a fifty-fifty chance of winning the next election, a bookie may say that the odds of India winning the next one-day cricket game is four to one, and so on.
At first glance, such probabilities may appear frequential ones, but a little reflection will reveal the contrary. We are concerned with unique events, i. It follows from the above deliberations that we have 2 types of probability concepts.The best sampling is probability sampling, because it increases the likelihood of obtaining samples that are representative of the population.
WHAT IS PROBABILITY? Probability is a recurring theme in medical practice. No doctor who returns home from a busy day at the hospital is spared the nagging feeling that some of his diagnoses may turn out to be wrong, or some of his treatments may not lead to the expected cure.
Probability: Types of Events. Life is full of random events! You need to get a "feel" for them to be a smart and successful person. The toss of a coin, throw of a dice and lottery draws are all examples of random events. Learn high school statistics for free—scatterplots, two-way tables, normal distributions, binomial probability, and more.
Full curriculum of exercises and videos. The following chart shows the relationship between the intersection of two events, the union of two events, and the complement of an event.
In each case, the probability in . Probability is the measure of the likelihood that an event will occur. See glossary of probability and statistics.
Probability is quantified as a number between 0 and 1, where, loosely speaking, 0 indicates impossibility and 1 indicates certainty.