Distinguish between null hypothesis and alternative hypothesis

Null hypothesis

This refers to a statement about a population parameter that is assumed to be true. Normally, it is denoted by Ho mathematically. It is also a statement that you want to test. In general, null hypothesis assume things are the same as each other or the same theoretical expectation.

Example: if you want to measure the height of adults, the null hypothesis could be that the average height of a normal man is the same as the average height of a normal woman.

Alternative hypothesis

This is a statement that contradicts the null hypothesis by stating the actual value of population parameter is not equal to, is less than or is greater than the value stated in null hypothesis. It is normally denoted by H1. Also, alternative hypothesis can be said to those things that are different from each other or different from the theoretical expectation.

Example:

If one is measuring the height of adults, the alternative hypothesis could be that the male adults have a different average height than women.

Distinguish between Type I error and Type II error

Type I Error

This is error that involves the rejection of a null hypothesis that is actually true. It is sometimes called an error of the first kind. Type I errors are equivalent to false positives. For example, a drug being used to treat a disease; if we reject the null hypothesis in this situation, then our claim is that the drug does in fact have some effect on a disease. But if the null hypothesis is true, then in reality the drug does not combat the disease at all. The drug is falsely claimed to have a positive effect on a disease.

Type I errors can be controlled. The value of alpha, which is related to the level of significance that we selected has a direct bearing on type I errors. Alpha is the maximum probability that we have a type I error. For a 95% confidence level, the value of alpha is 0.05. This means that there is a 5% probability that we will reject a true null hypothesis. In the long run, one out of every twenty hypothesis tests that we perform at this level will result in a type I error.

Type II Error

This is error that occurs when we do not reject a null hypothesis that is false. This sort of error is also referred to as an error of the second kind. Type II errors are equivalent to false negatives. For instance, when we are testing a drug, a type II error would occur if we accepted that the drug had no effect on a disease, but in reality it did. The probability of a type II error is given by the Greek letter beta. This number is related to the power or sensitivity of the hypothesis test, denoted by 1 – beta.

What Is the Function of the Hypothesis

Identification

A hypothesis is an educated guess, based on the probability of an outcome. Scientists formulate hypotheses after they understand all the current research on their subject. Hypotheses specify the relationship between at least two variables, and are testable. For a hypothesis to function properly, other scientists must be able to reproduce the results that prove or disprove it. Two types of hypotheses exist: a descriptive hypothesis asks a question, and a directional hypothesis makes a statement.

Scientific Method

The scientific method is the process by which hypotheses function. Scientists use the scientific method to, over time, form an accurate picture of the world. The scientific method attempts to remove the scientist’s bias from the research. The four parts of the scientific method are observation and description, formulation of a hypothesis, use of the hypothesis for prediction and performance of testing of the hypothesis. Scientists use the scientific method to disprove hypotheses, rather than prove them. It they cannot be disproved, the hypotheses over time become accepted theories.

Experiments

The most important function hypotheses perform is providing the framework for testing and experimentation. Scientists formulate a hypothesis, or ask a question, about a certain phenomenon and how it relates to other aspects of the world. Then they devise ways to try to disprove their theory as to the answer. For instance, if a scientist made a hypothesis that what goes up must come down, he would test it by throwing many items in the air to see if they do come down. Because scientists cannot test every single possible item for this theory, hypotheses are never proven. However, after many scientists have experimented with the hypothesis, it becomes accepted scientific theory.

Formulating other Hypotheses

Scientists make a hypothesis by comparing the phenomenon being studied to another phenomenon. For instance, in the real world, a person might decide that her house is cold because a window is open. She would test this theory by checking the windows. If the windows are closed, then that hypothesis is proven false, and another is formed when the person decides that her house is probably cold because the furnace isn’t working properly. The process of forming and disproving hypotheses continues until a person makes a hypothesis that cannot be disproved.