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Experimenters should utilize randomized controlled trials, where participants are randomly assigned to experimental conditions, to accurately infer cause and effect. This design helps control for confounding variables and establish a causal relationship between the independent and dependent variables. Additionally, ensuring proper blinding and matching techniques can also enhance the accuracy of causal inferences in experiments.
opinion or personal bias. It should focus on presenting a clear relationship between a specific event (the cause) and its resulting consequences (the effect).
The cause is the reason something happens, while the effect is the result of that cause. Cause and effect are linked in that the cause triggers an action or event that leads to the effect. Identifying the cause and effect relationship helps in understanding how events are connected and predicting outcomes.
Cause: Heavy rain, Effect: Flooding in the streets. Cause: Lack of exercise, Effect: Weight gain. Cause: Studying regularly, Effect: Higher exam scores. Cause: Excessive drinking, Effect: Liver damage. Cause: Not wearing a helmet, Effect: Head injury in an accident. Cause: Poor diet, Effect: Vitamin deficiencies. Cause: Smoking, Effect: Increased risk of lung cancer. Cause: Speeding, Effect: Car accidents. Cause: Not getting enough sleep, Effect: Poor concentration. Cause: Improper waste disposal, Effect: Environmental pollution.
Cause and effect is a relationship between events or things, where one event (the cause) leads to the occurrence of another event (the effect). The cause is what makes something happen, while the effect is the result of that cause. Understanding cause and effect helps in analyzing relationships and predicting outcomes in various situations.
Cause and effect is the relationship between events where one event (the cause) leads to another event (the effect). For example, eating unhealthy food (cause) can lead to weight gain (effect). Another example is rain (cause) leading to wet streets (effect).