Hypothesis in Business Research (Unit 2)

 

What is Hypothesis?

 

·         Hypothesis means a mere assumption or some supposition to be proved or disproved.

·         Hypothesis may be defined as proposition or a set of proposition set forth as an explanation for the occurrence of some specified group of phenomena either asserted merely as a provisional conjecture to guide some investigation or accepted as highly probable in the light of established facts.

·         It is a predictive statement, capable of being tested by scientific methods. 

 "E.g. students who receive counselling. show greater creativity".

 

 Characteristics of Hypothesis

·         Should be clear & precise

·         Capable of being tested.

·         Hypothesis should state relationship between variables.

·         Should be limited in scope, narrower hypothesis are the ones generally more testable..

·         Should he consistent with most known facts, consistent with a substantial body of established facts, i.e. judges must accept as being the most likely.

·         Should be amenable to testing within a reasonable time.

 

 Types of Hypothesis

There are six forms of hypothesis and they are:

·         Simple hypothesis

·         Complex hypothesis

·         Directional hypothesis

·         Non-directional hypothesis

·         Null hypothesis

·         Associative and casual hypothesis

·         Empirical Hypothesis

 

 Simple Hypothesis

It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable.

 

Complex Hypothesis

It shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease.

 

Directional Hypothesis

It shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect.

 

Non-directional Hypothesis

It is used when there is no theory involved. It is a statement that a relationship exists between two variables, without predicting the exact nature (direction) of the relationship.

 

Null Hypothesis

It provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “HO”.

 

Associative and Causal Hypothesis

Associative hypothesis occurs when there is a change in one variable resulting in a change in the other variable. Whereas, the causal hypothesis proposes a cause and effect interaction between two or more variables.

 

 Empirical Hypothesis

An empirical hypothesis is a statement based on things we can see and measure. It comes from direct observation or experiments and can be tested with real-world evidence.

 

 Sources of Hypothesis

Following are the sources of hypothesis:

The resemblance between the phenomenon.

Observations from past studies,

Present-day experiences and from the competitors.

Scientific theories.

General patterns that influence the thinking process of people.

 

 Null Hypothesis & Alternate Hypothesis

 

Key Considerations for Hypothesis Testing 1. Alternative Hypothesis and Null Hypothesis In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis. Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis, on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.   For example, consider a company that historically and reliably sees Rs12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.   In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our product by five percent, then we’ll see an increase in sales and realize revenues greater than Rs12 million in the next month.” The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of Rs12 million, or might even decrease.

 

 

Definition of Null Hypothesis

Null hypothesis suggests that there is no relationship between the two variables.

A null hypothesis is a statistical hypothesis in which there is no significant difference exist between the set of variables. It is the original or default statement, with no effect, often represented by H₀ (H-zero). It is always the hypothesis that is tested.

 

Definition of Alternative Hypothesis

A statistical hypothesis used in hypothesis testing, which states that there is a significant difference between the set of variables. It is often referred to as the hypothesis other than the null hypothesis, often denoted by H1 (H-one).

 

Examples

1.Research question.

What are the health benefits of eating an apple a day?

Alternate Hypothesis : Increasing apple consumption in over (r aged people) will result in decreasing frequency of doctor visit.

Nul Hypothesis : Increasing apple consumption in over (r aged people) will have no effect on frequency of doctors visit

 

2.Research question.

What effect does daily use of social media have on the attention span of under (x aged people)

Alternate Hypothesis: There is negative correlation between time spent on social media and attention span in under (x).

Nul Hypothesis : There is no relationship between social media and attention span in under (x aged people).

 

3.Research question.

Flexi working hours give job satisfaction.

Alternate Hypothesis: Employees that have flexible working hours will have greater job satisfaction than those who work fixed hours.

Null Hypothesis : There is no relationship between working hour flexibility and job satisfaction.

 

4.Research Question

55% boys seem taller than girls.

Alternative hypothesis is that 55% of boys in my town are taller than girls.

Then my null hypothesis will be that 55% of boys in my town are not taller than girls.

 

If my null hypothesis is that 55% of boys in my town are not taller than girls then my alternative hypothesis will be that 55% of boys in my town are taller than girls.

 

 

Key Differences Between Null and Alternative Hypothesis

The important points of differences between null and alternative hypothesis are explained as under:

1.      A null hypothesis is a statement, in which there is no relationship between two variables. An alternative hypothesis is a statement; that is simply the inverse of the null hypothesis, i.e. there is some statistical significance between two measured phenomena.

2.      A null hypothesis is what, the researcher tries to disprove whereas an alternative hypothesis is what the researcher wants to prove.

3.      A null hypothesis represents, no observed effect whereas an alternative hypothesis reflects, some observed effect.

4.      If the null hypothesis is accepted, no changes will be made in the opinions or actions. Conversely, if the alternative hypothesis is accepted, it will result in the changes in the opinions or actions.

5.      A null hypothesis is labelled as H0 (H-zero) while an alternative hypothesis is represented by H1 (H-one).

 

Level of Significance

In statistics, the level of significance, often denoted as alpha (α), is the probability of rejecting the null hypothesis when it is actually true, essentially a threshold for determining statistical significance. 

Here's a more detailed explanation:

What it is:

The level of significance is a pre-determined probability that a researcher sets before conducting a statistical test. 

Common values:

A common level of significance is 0.05 (or 5%), meaning there's a 5% chance of incorrectly rejecting the null hypothesis. Other values include 0.01 (1%) or 0.10 (10%). 

The 5 % level of Significance means that researcher is willing to take as much as a 5% risk of rejecting the null hypothesis when it (Ho) happens to be true.

Null hypothesis:

The null hypothesis (Ho) is a statement of no effect or no difference, which the researcher aims to disprove. 

 

 

Key Considerations for Hypothesis Testing

1. Alternative Hypothesis and Null Hypothesis

In hypothesis testing, the hypothesis that’s being tested is known as the alternative hypothesis. Often, it’s expressed as a correlation or statistical relationship between variables. The null hypothesis, on the other hand, is a statement that’s meant to show there’s no statistical relationship between the variables being tested. It’s typically the exact opposite of whatever is stated in the alternative hypothesis.

 

For example, consider a company that historically and reliably sees Rs12 million in monthly revenue. They want to understand if reducing the price of their services will attract more customers and, in turn, increase revenue.

 

In this case, the alternative hypothesis may take the form of a statement such as: “If we reduce the price of our product by five percent, then we’ll see an increase in sales and realize revenues greater than Rs12 million in the next month.”

The null hypothesis, on the other hand, would indicate that revenues wouldn’t increase from the base of Rs12 million, or might even decrease.

 

 

Errors in Hypothesis Testing

 

What are type I errors?

A type I error happens when you run an experiment and wrongly conclude that the change you tested impacted your target metric.With type I errors, you see an effect that’s not there and reject your null hypothesis based on the observation.

Your probability of making a type I error depends on your experiment’s significance level. Generally, a web experiment's significance level is set at 5% or 0.05, which means your chance of making a type I error is 5%.

Understanding type I errors

When you begin your experiment, you set its significance level threshold (α). Your experimentation solution then compares your test’s p-value (the probability of obtaining test results at least as extreme or more extreme as the results actually observed) with the significance level you set. If your test’s p-value is lower than your significance level threshold (α), then you're looking at statistically significant results, and you can reject your null hypothesis safely and avoid making a type I error.

 

What are type II errors?

A type II error is when you run an experiment and conclude that the change you tested didn’t impact your target metric when, in reality, it did. In other words, with type II errors, you miss the effect your experiment produces and fail to reject your null hypothesis.

Your probability of making a type II error depends on your experiment’s statistical power. Generally, statistical power is set at 80% or 0.80, which means there’s an 80% chance that your experiment will be able to detect any actual effect that your experiment causes. This also means there’s a 20% chance that you could miss the real impact of your experiment and make a type II error. 

Understanding type II errors

Before you set up your experiment, you run a "power analysis" to determine the sample size you'll need to achieve your desired power level, significance level, and the expected effect size. Then, you input this sample size into your experimentation solution.

 

Once your experimentation solution delivers your experiment to the target sample sizes and it runs its intended length, you have a winner. Running an adequately powered test for its intended length is how you detect any true effects that your experiment causes and avoid creating type II errors.