Hasan's Cake Experiment: Setup and Results

Hasan, a child, investigates how the amount of sugar affects cake size. The example shows the experiment from observation to revised conclusion.

A teacher who can present this example clearly helps students see how the scientific method works in real situations.

The story begins: Hasan’s observation

This is curious observation. Hasan notices that:

1. The mixture in the mold has one size (small, level).
2. The cake that comes out of the oven has another size (bigger).
3. The size has changed during baking.

A non-curious child watching the same process might not notice. They might just wait for the cake to be ready. Hasan, however, observes the size change. The observation prompts a question.

The first question

Hasan asks his mother or grandmother:

“Why has its size increased?”

This is the start of the scientific method. A specific, real question based on observation.

The question is simple but real. Hasan does not know the answer. He wants to know.

Hasan knows that matter is not created or destroyed (mass is conserved). But the cake’s size grows. There is an apparent contradiction. The question deepens.

This is good scientific thinking. Hasan is connecting his observation to prior knowledge and noticing where the prior knowledge does not fit.

Background research

Hasan’s grandmother or mother explains:

“Actually, when we add baking powder in it, if we add baking powder, then carbon dioxide is released, which is a gas, and it helps to increase the size of the cake. And if we are not adding baking powder, then we may add yeast in it, and due to yeast also, carbon dioxide is produced during the process of fermentation.”

This is background information. Hasan now knows:

1. Baking powder releases carbon dioxide.
2. Yeast produces carbon dioxide through fermentation.
3. Carbon dioxide gas expands the cake.

The science: gas takes up space. As carbon dioxide forms in the dough, it pushes the dough outward, making the cake larger.

Hasan continues with another question:

“What happens in the process of fermentation?”

His grandmother explains:

“All the carbohydrates in the flour [are converted] to sugar. And apart from this, [the sugar] gets converted.. And the sugar that we add is directly sugar, and the yeast eats the sugar. As a result, carbon dioxide is produced.”

Now Hasan knows:

1. Yeast eats sugar.
2. Yeast produces carbon dioxide.
3. More carbon dioxide means more cake expansion.

This conversation is part of background research. Hasan has gathered information from a knowledgeable source (his grandmother).

Hasan now widens his research to find a way to test his question. He looks at:

1. Books on baking.
2. Magazines on fermentation.
3. Internet sources.

He keeps a journal of what he learns. This is good research practice.

A new question emerges

From his research, Hasan develops a more specific question: does the amount of sugar used in the recipe affect the cake size?

If yeast eats sugar to produce carbon dioxide, then more sugar should produce more carbon dioxide, which should make a bigger cake. This is an inferred connection.

Hasan turns this into a testable question: does more sugar produce a bigger cake?

He talks to his teacher. The teacher helps him design an experiment.

The hypothesis

Hasan formulates his hypothesis:

“If sugar is increased, then the size of the cake will increase.”

This is a specific, testable, falsifiable hypothesis.

It is also “educated.” Hasan made the guess based on:

1. Conversation with his grandmother.
2. Reading on yeast and fermentation.
3. Logic: more food for yeast leads to more gas leads to bigger cake.

Hasan’s hypothesis is educated, not random.

The experimental design

The teacher helps Hasan design the experiment. The design includes:

Title: “The Effect of Sugar on the Size of Cake.”

Hypothesis: “If sugar is increased, then the size of the cake will increase.”

Variables:

• Independent variable (the one changed): amount of sugar.
• Dependent variable (the one measured): cake size.

Constants:

• Type of oven.
• Temperature.
• Baking time.
• Other ingredients (flour, eggs, butter, etc.).
• Recipe steps.
• Type of cake mold.

By changing only sugar amount and holding everything else constant, Hasan can attribute any size differences to sugar.

Sugar amounts to test: 25g, 50g, 100g, 250g, 500g. A range.

Number of trials per amount: Three.

Flashcard

What were the variables and constants in Hasan's cake experiment?

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Answer

Sugar amount changed; cake size measured; everything else held fixed

Independent variable: amount of sugar (25g, 50g, 100g, 250g, 500g).

Dependent variable: cake size in cm cubed.

Constants: oven, temperature, baking time, other ingredients, recipe steps, mold.

Three trials per amount let Hasan calculate averages and see real patterns rather than fluke results.

Running the experiment

Hasan runs the experiment. Each trial:

1. Mix all ingredients (with the specified amount of sugar).
2. Pour into the mold.
3. Bake for the constant time at the constant temperature.
4. Measure the cake size after baking.

He records observations for each trial.

The data

The exact numbers:

• 25 grams sugar: average size around 758 cm cubed.
• 50 grams sugar: average size around 1260 cm cubed (much bigger).
• 100 grams sugar: average size around 1116 cm cubed (smaller than 50g).
• 250 grams sugar: size further reduced.
• 500 grams sugar: size much reduced (close to or below 25g).

The pattern

Surprising. The hypothesis predicted a linear relationship: more sugar means bigger cake. But the data shows:

1. From 25g to 50g: size increases significantly.
2. From 50g to 100g: size decreases slightly.
3. From 100g to 250g: size decreases more.
4. From 250g to 500g: size decreases dramatically.

The relationship is not linear. There is an optimal amount, somewhere around 50g. Above that, more sugar reduces the size.

The hypothesis is rejected

The data does not support the hypothesis. Sugar amount and cake size are not in a simple direct relationship.

What does Hasan do? Give up? No. He forms a new hypothesis.

This is the iterative nature of the scientific method. A rejected hypothesis is information. Hasan now knows that the simple “more sugar means bigger cake” is wrong. He revises.

The new hypothesis

Hasan’s new hypothesis: the amount that actually increases the size of the cake is probably between 50 and 100 grams.

This is a more refined hypothesis. The optimum is between 50 and 100 grams. He needs to test more carefully in this range.

This new hypothesis is also educated. It is based on the data from the first experiment. He observed where the peak appeared and is now looking more carefully there.

The second experiment

Hasan tests in the 50-70 gram range:

• 50 grams: average size around 1344 cm cubed.
• 60 grams: average size around 1380 cm cubed.
• 70 grams: average size around 1612 cm cubed.

(After 70g, sizes decrease again.)

The pattern: size increases from 50g to 70g, then decreases beyond 70g.

The revised conclusion

Hasan’s conclusion:

“If the amount of sugar is increased from 50 to 70, then the size increases. Otherwise, it decreases.”

This is more accurate than the original hypothesis. There is an optimal sugar amount around 70g. More than 70g actually reduces cake size.

The science continues. Hasan could test further:

1. Is the optimum exactly 70g, or is it 65g or 75g?
2. Why does too much sugar reduce cake size?
3. Does the relationship change with different yeast amounts?
4. Does altitude or temperature affect this?

Each new question generates a new hypothesis. The cycle continues.

Flashcard

What does Hasan's data show about the relationship between sugar amount and cake size?

Tap to reveal

Answer

Cake size increases with sugar up to about 70g, then decreases beyond that

The first hypothesis (more sugar means bigger cake) predicted a linear relationship. The data showed a non-linear one with a peak.

25g produces a small cake. 50-70g produces the biggest. 100g, 250g, and 500g produce progressively smaller cakes.

The optimal is around 70g. Above that, more sugar actively reduces cake size. The first hypothesis was rejected; a more refined hypothesis fit the data.