Hypothesis

Hypothesis drives inquiry and discovery. From analysing the behaviour of the consumers to predicting rainfall patterns, hypothesis has been the fundamental concept behind countless innovations. As a student, you need to understand what a hypothesis is and how it is key to critical thinking. Let’s dive in!

1.0What is a Hypothesis?

When a person begins an investigation or an experiment, in some cases, the starting point is proposing an explanation for a phenomenon. It connects theory with observation.

So, what is a hypothesis? In simple terms, it is an educated guess that can be tested through experiments or data. The hypothesis meaning in scientific research, refers to a statement that is either supported or rejected through experimentation. 

2.0Hypothesis Testing 

Here are the main types of hypotheses used in research:

3.0Procedure of Hypothesis Testing

Step 1: State the Research Hypothesis

The first step is to clearly formulate the research hypothesis, which represents the statement you intend to test. This is also known as the alternative hypothesis and is denoted by Ha. It predicts that there is a significant relationship between the variables involved.

Alongside the alternative hypothesis, you also need to define the null hypothesis (H₀). The null hypothesis represents the default position, asserting that no relationship exists between the variables.

Example – Suppose we want to test whether men are, on average, taller than women:

• H₀ (Null Hypothesis): On average, men are not taller than women.
• Ha (Alternative Hypothesis): On average, men are taller than women.

Only one of these statements can be true, and the process of hypothesis testing will help us determine which one the data supports.

Step 2: Collect Relevant Sample Data

Once the hypotheses are defined, the next step is to collect sample data. This process, known as sampling, should be conducted carefully to ensure that the sample represents the larger population.

Your sample must be:

• Random and unbiased
• Sufficiently large
• Relevant to the population you're studying

This ensures that your hypothesis test results are reliable and can be generalised.

Step 3: Choose the Appropriate Hypothesis Testing Method

Depending on your data type and research question, you'll need to select a statistical test such as:

• t-test (for comparing two means)
• z-test (for large samples with known variance)
• ANOVA (for comparing more than two means)
• Chi-square test (for categorical variables)

Choosing the correct method ensures that your hypothesis testing is both accurate and meaningful.

Step 4: Understand and Calculate the P-Value

The p-value helps us measure how likely it is to get the observed results if the null hypothesis (H₀) is actually true. It plays a crucial role in deciding whether the evidence from your data is strong enough to reject the null hypothesis.

• A low p-value (usually ≤ 0.05) means the results are unlikely to have occurred by random chance, suggesting strong evidence against H₀. In this case, we reject the null hypothesis.
• A high p-value (> 0.05) means the results could easily occur under the assumption that H₀ is true, so we do not reject the null hypothesis.

Step 5: Make a Decision

Based on the p-value and the significance level (commonly 0.05), you decide:

• Whether to accept the alternative hypothesis (Ha)
• Or to retain the null hypothesis (H₀)

Example Decision:

If your calculated p-value is 0.03, you reject H₀ and conclude that men are significantly taller than women.

Step 6: Interpret the Results

Finally, interpret the result in context. Explain what the outcome means in relation to your original research question. Always consider:

• The real-world implications
• The sample limitations
• The possibility of Type I and Type II errors

4.0Real-Life Hypothesis Testing Examples

To understand the meaning of hypothesis testing, let’s examine a few real-world scenarios where this method is applied. These hypothesis examples highlight how researchers use hypotheses to evaluate claims and draw conclusions from data.

Example 1: Physical Therapy and Recovery Time

The average recovery time after knee surgery is known to be 8 weeks. A doctor hypothesises that patients attending physical therapy twice a week instead of thrice will take longer to recover.

Hypothesis Formulation:

• Null Hypothesis (H₀): There is no difference in recovery time based on the frequency of physical therapy.
• Alternative Hypothesis (H₁): Patients attending therapy twice a week recover more slowly than those attending three times a week.

The goal of this is to conduct a hypothesis test to see if reducing the frequency of therapy significantly increases recovery time.

Example 2: Life Cycle Hypothesis

The life cycle hypothesis (LCH) is an economic theory proposed by Franco Modigliani and Richard Brumberg in the 1950s. It suggests that people plan their spending and saving based on expected lifetime income. 

Individuals borrow when young, save during middle age, and spend savings in retirement. This creates a hump-shaped wealth pattern—low in youth and old age, high in middle age. The LCH implies that younger individuals can take more investment risks, while older people rely on accumulated savings. 

Although insightful, the theory’s assumptions may not apply universally across real-life scenarios.