Statistical significance is about whether an observed effect is unlikely to have occurred by chance, given a null hypothesis and a chosen threshold. It uses probability to decide if the result could be due to random variation, not whether the effect matters in real-world practice. Clinical significance, by contrast, asks whether the size of the effect is large enough to be meaningful for patients and care settings. You can have a result that is statistically significant but clinically trivial if the sample is large and the effect is small, and you can have a clinically important effect that isn’t statistically significant in a small study due to limited power. That contrast is why the statement linking statistical significance to the likelihood that results are not due to chance is the best way to distinguish the two concepts.

In case conceptualization, the focus is on factors that are evidence-based and clinically useful for explaining how a client’s difficulties arose and how they might change with treatment. Family history provides insight into genetic predispositions and relational dynamics that can influence risk and maintenance of symptoms. Psychosocial stressors capture the client’s current life events, supports, and environmental pressures that can trigger or sustain distress. Symptom patterns describe the trajectory, intensity, and functional impact of the presenting problems, helping to identify targets for intervention and monitor progress over time. Astrology or horoscope factors, on the other hand, lack empirical support as predictors or explanations of mental health issues and would not inform a scientifically grounded case formulation. Including such factors could distract from evidence-based considerations and treatment planning.

Think of this in terms of areas under the normal curve. The interval from -2 to +1 standard deviations covers the portion of the curve two standard deviations below the mean up to one standard deviation above the mean. Using standard normal values, the area up to +1 is about 0.8413, and the area up to -2 is about 0.0228. The area between them is 0.8413 − 0.0228 ≈ 0.8185, or about 81.9%, which rounds to 82%. If you break it down another way: from -2 to 0 is 0.5 − 0.0228 = 0.4772, and from 0 to +1 is 0.3413; add them to get 0.4772 + 0.3413 = 0.8185, confirming the same result. This interval isn’t symmetric around the mean, so it doesn’t match the common rules like ±1 SD (about 68%), ±2 SD (about 95%), or ±3 SD (about 99%).

The sociological model treats career development as shaped by social context, family expectations and resources, culture, ethnicity, and broader social structures, rather than by biology. Emphasizing genetic predispositions as the sole determinant doesn’t fit this view, because it centers innate biological factors instead of how people’s opportunities, norms, and environments influence their career paths. Relocation requirements can reflect job markets, family obligations, and community norms that constrain or enable mobility—clear social factors the sociological perspective would consider. Ethnicity and culture influence occupational access, expectations, and meaning attached to work, which again aligns with this model. And the emphasis on social context and family influences is a direct hallmark of sociological thinking, since these elements shape choices, access, and outcomes in careers.